Subjects -> LIBRARY AND INFORMATION SCIENCES (Total: 392 journals)
    - DIGITAL CURATION AND PRESERVATION (13 journals)
    - LIBRARY ADMINISTRATION (1 journals)
    - LIBRARY AND INFORMATION SCIENCES (378 journals)

LIBRARY AND INFORMATION SCIENCES (378 journals)                  1 2 | Last

Showing 1 - 200 of 379 Journals sorted alphabetically
027.7 Zeitschrift für Bibliothekskultur / Journal for Library Culture     Open Access   (Followers: 61)
Access     Full-text available via subscription   (Followers: 23)
Acervo : Revista do Arquivo Nacional     Open Access   (Followers: 1)
African Journal of Library, Archives and Information Science     Full-text available via subscription   (Followers: 67)
Against the Grain     Partially Free   (Followers: 121)
AIB Studi     Full-text available via subscription   (Followers: 10)
Alexandría : Revista de Ciencias de la Información     Open Access   (Followers: 11)
Alexandria : The Journal of National and International Library and Information Issues     Full-text available via subscription   (Followers: 55)
Alsic : Apprentissage des Langues et Systèmes d'Information et de Communication     Open Access   (Followers: 12)
American Archivist     Hybrid Journal   (Followers: 131)
American Libraries     Partially Free   (Followers: 186)
Anales de Documentacion     Open Access   (Followers: 13)
Anuari de l'Observatori de Biblioteques, Llibres i Lectura     Open Access   (Followers: 2)
ANZTLA EJournal     Full-text available via subscription  
Archeion Online     Open Access   (Followers: 3)
Archimag     Full-text available via subscription   (Followers: 3)
Archival Science     Hybrid Journal   (Followers: 64)
Archivaria     Open Access   (Followers: 32)
Archives     Full-text available via subscription   (Followers: 6)
Archives and Manuscripts     Hybrid Journal   (Followers: 50)
Archives and Museum Informatics     Hybrid Journal   (Followers: 97)
Ariadne Magazine     Open Access   (Followers: 147)
Art Libraries Journal     Hybrid Journal   (Followers: 10)
Aslib Journal of Information Management     Hybrid Journal   (Followers: 32)
Aslib Proceedings     Hybrid Journal   (Followers: 148)
AtoZ : novas práticas em informação e conhecimento     Open Access  
Australasian Journal of Information Systems     Open Access   (Followers: 16)
Australasian Public Libraries and Information Services     Full-text available via subscription   (Followers: 31)
Australian Academic & Research Libraries     Full-text available via subscription   (Followers: 92)
Australian Library Journal     Full-text available via subscription   (Followers: 148)
Baca : Jurnal Dokumentasi dan Informasi     Open Access   (Followers: 1)
Bangladesh Journal of Library and Information Science     Open Access   (Followers: 44)
Behavioral & Social Sciences Librarian     Hybrid Journal   (Followers: 144)
Berkala Ilmu Perpustakaan dan Informasi     Open Access  
Biblios     Open Access   (Followers: 11)
Biblioteca Escolar em Revista     Open Access  
Biblioteca Universitaria     Open Access   (Followers: 14)
Bibliotecas : Revista de la Escuela de Bibliotecología, Documentación e Información     Open Access   (Followers: 3)
Bibliotecas Universitárias : pesquisas, experiências e perspectivas     Open Access   (Followers: 1)
Bibliotecas. Anales de Investigacion     Open Access  
Biblioteka     Open Access   (Followers: 2)
Biblioteka i Edukacja     Open Access   (Followers: 4)
Bibliotheca Orientalis     Full-text available via subscription   (Followers: 14)
BIBLIOTIKA : Jurnal Kajian Perpustakaan dan Informasi     Open Access  
BIBLOS - Revista do Departamento de Biblioteconomia e História     Open Access   (Followers: 6)
BiD : textos universitaris de biblioteconomia i documentació     Open Access   (Followers: 6)
Bilgi Dünyası     Open Access   (Followers: 5)
Biodiversity Information Science and Standards     Open Access   (Followers: 1)
Bioinformatics     Hybrid Journal   (Followers: 217)
Biuletyn EBIB     Open Access  
Boletín Cultural y Bibliográfico     Open Access   (Followers: 2)
Book History     Full-text available via subscription   (Followers: 116)
Bridgewater Review     Open Access   (Followers: 4)
Bulletin des bibliotheques de France     Full-text available via subscription   (Followers: 7)
Bulletin of the Association for Information Science and Technology     Open Access   (Followers: 23)
Bulletin of the John Rylands Library     Hybrid Journal   (Followers: 21)
Canadian Journal of Academic Librarianship     Open Access   (Followers: 20)
Canadian Journal of Information and Library Science     Full-text available via subscription   (Followers: 247)
Cataloging & Classification Quarterly     Hybrid Journal   (Followers: 170)
CERN IdeaSquare Journal of Experimental Innovation     Open Access  
Children and Libraries : The Journal of the Association for Library Service to Children     Full-text available via subscription   (Followers: 16)
CIC. Cuadernos de Informacion y Comunicacion     Open Access   (Followers: 4)
Ciência da Informação em Revista     Open Access   (Followers: 1)
Code4Lib Journal     Open Access   (Followers: 174)
Collaborative Librarianship     Open Access   (Followers: 52)
Collection and Curation     Hybrid Journal   (Followers: 11)
College & Research Libraries     Open Access   (Followers: 454)
College & Research Libraries News     Partially Free   (Followers: 246)
College & Undergraduate Libraries     Hybrid Journal   (Followers: 221)
Communicate : Journal of Library and Information Science     Full-text available via subscription   (Followers: 63)
Communication Booknotes Quarterly     Hybrid Journal   (Followers: 15)
Communications in Information Literacy     Open Access   (Followers: 196)
Community & Junior College Libraries     Hybrid Journal   (Followers: 43)
Cuadernos de Gestión de Información     Open Access  
Data Curation Profiles Directory     Open Access   (Followers: 5)
Data Technologies and Applications     Hybrid Journal   (Followers: 209)
DESIDOC Journal of Library & Information Technology     Open Access   (Followers: 96)
Digital Library Perspectives     Hybrid Journal   (Followers: 39)
Digital Platform: Information Technologies in Sociocultural Sphere     Open Access   (Followers: 1)
Documentación de las Ciencias de la Información     Open Access  
Documentation et bibliothèques     Full-text available via subscription   (Followers: 9)
e & i Elektrotechnik und Informationstechnik     Hybrid Journal   (Followers: 8)
e-Ciencias de la Información     Open Access   (Followers: 1)
Eastern Librarian     Open Access   (Followers: 11)
Edulib : Journal of Library and Information Science     Open Access   (Followers: 26)
Egyptian Informatics Journal     Open Access   (Followers: 5)
El Profesional de la Informacion     Full-text available via subscription   (Followers: 17)
eLucidate     Open Access   (Followers: 7)
Emerging Library & Information Perspectives     Open Access   (Followers: 29)
Encontros Bibli : revista eletrônica de biblioteconomia e ciência da informação     Open Access   (Followers: 3)
Ethics and Information Technology     Hybrid Journal   (Followers: 64)
European Journal of Information Systems     Hybrid Journal   (Followers: 85)
European Science Editing     Open Access  
Evidence Based Library and Information Practice     Open Access   (Followers: 386)
Florida Libraries     Open Access   (Followers: 1)
Folia Bibliologica     Open Access  
Forensic Science International: Digital Investigation     Full-text available via subscription   (Followers: 317)
Foundations and Trends® in Information Retrieval     Full-text available via subscription   (Followers: 30)
Georgia Library Quarterly     Open Access   (Followers: 21)
Ghana Library Journal     Full-text available via subscription   (Followers: 16)
Global Knowledge, Memory and Communication     Hybrid Journal   (Followers: 804)
GSI Journals Serie C : Advancements in Information Sciences and Technologies     Open Access   (Followers: 1)
Health Information Management Journal     Hybrid Journal   (Followers: 23)
Hipertext.net : Anuario Académico sobre Documentación Digital y Comunicación Interactiva     Open Access  
HLA News     Full-text available via subscription   (Followers: 2)
IASSIST Quarterly     Open Access  
Idaho Librarian     Free   (Followers: 8)
IFLA Journal     Hybrid Journal   (Followers: 219)
In Monte Artium     Full-text available via subscription   (Followers: 1)
In the Library with the Lead Pipe     Open Access   (Followers: 123)
InCID : Revista de Ciência da Informação e Documentação     Open Access  
InCite     Full-text available via subscription   (Followers: 19)
Informaatiotutkimus     Open Access   (Followers: 3)
Informação & Informação     Open Access   (Followers: 2)
Informação em Pauta     Open Access  
Informacijos mokslai     Open Access  
Información, Cultura y Sociedad     Open Access   (Followers: 2)
Informatio. Revista del Instituto de Información de la Facultad de Información y Comunicación     Open Access  
Information     Open Access   (Followers: 30)
Information & Culture : A Journal of History     Full-text available via subscription   (Followers: 31)
Information Discovery and Delivery     Hybrid Journal   (Followers: 43)
Information Manager (The)     Open Access   (Followers: 29)
Information Processing & Management     Hybrid Journal   (Followers: 127)
Information Retrieval     Hybrid Journal   (Followers: 190)
Information Sciences     Hybrid Journal   (Followers: 172)
Information Systems Frontiers     Hybrid Journal   (Followers: 27)
Information Systems Research     Full-text available via subscription   (Followers: 130)
Information Technologies & International Development     Open Access   (Followers: 81)
Information Technologist (The)     Full-text available via subscription   (Followers: 17)
Information Technology and Libraries     Open Access   (Followers: 295)
Information Today     Full-text available via subscription   (Followers: 34)
Informationspraxis     Open Access   (Followers: 12)
Informationswissenschaft : Theorie, Methode und Praxis     Open Access   (Followers: 4)
iNFOTEZY     Open Access  
Insaniyat : Journal of Islam and Humanities     Open Access   (Followers: 1)
Insights : the UKSG journal     Open Access   (Followers: 62)
InterActions: UCLA Journal of Education and Information     Open Access   (Followers: 11)
Interdisciplinary Journal of e-Skills and Lifelong Learning     Open Access   (Followers: 3)
Interdisciplinary Journal of Information, Knowledge, and Management     Open Access   (Followers: 12)
International Association of School Librarianship Conference Proceedings     Open Access  
International Information & Library Review     Hybrid Journal   (Followers: 395)
International Journal of Bibliometrics in Business and Management     Hybrid Journal   (Followers: 2)
International Journal of Business Information Systems     Hybrid Journal   (Followers: 14)
International Journal of Cooperative Information Systems     Hybrid Journal   (Followers: 4)
International Journal of Digital Curation     Open Access   (Followers: 81)
International Journal of Digital Library Systems     Full-text available via subscription   (Followers: 73)
International Journal of Doctoral Studies     Open Access   (Followers: 6)
International Journal of Information and Decision Sciences     Hybrid Journal   (Followers: 10)
International Journal of Information Management     Hybrid Journal   (Followers: 157)
International Journal of Information Privacy, Security and Integrity     Hybrid Journal   (Followers: 25)
International Journal of Information Retrieval Research     Full-text available via subscription   (Followers: 28)
International Journal of Information Science and Management     Open Access   (Followers: 5)
International Journal of Information Technology, Communications and Convergence     Hybrid Journal   (Followers: 14)
International Journal of Information, Diversity, & Inclusion     Open Access   (Followers: 3)
International Journal of Intellectual Property Management     Hybrid Journal   (Followers: 26)
International Journal of Intercultural Information Management     Hybrid Journal   (Followers: 12)
International Journal of Legal Information     Full-text available via subscription   (Followers: 49)
International Journal of Librarianship     Open Access   (Followers: 25)
International Journal of Library and Information Science     Open Access   (Followers: 232)
International Journal of Library Science     Open Access   (Followers: 262)
International Journal of Library Science     Full-text available via subscription   (Followers: 55)
International Journal of Multicriteria Decision Making     Hybrid Journal   (Followers: 8)
International Journal of Multimedia Information Retrieval     Partially Free   (Followers: 8)
International Journal of Organisational Design and Engineering     Hybrid Journal   (Followers: 3)
International Journal of Web Portals     Full-text available via subscription   (Followers: 16)
International Journal on Digital Libraries     Hybrid Journal   (Followers: 545)
InULA Notes : Indiana University Librarians Association     Open Access  
Investigación Bibliotecológica     Open Access   (Followers: 4)
IRIS - Revista de Informação, Memória e Tecnologia     Open Access  
Issues in Informing Science and Information Technology     Open Access   (Followers: 2)
Issues in Science and Technology Librarianship     Open Access   (Followers: 2)
JISTEM : Journal of Information Systems and Technology Management     Open Access   (Followers: 6)
JLIS.it     Open Access   (Followers: 7)
JMIR Medical Informatics     Open Access   (Followers: 10)
Journal of Academic Librarianship     Hybrid Journal   (Followers: 1012)
Journal of Access Services     Hybrid Journal   (Followers: 39)
Journal of Advancements in Library Sciences     Open Access   (Followers: 47)
Journal of Adventist Libraries and Archives     Open Access  
Journal of Altmetrics     Open Access   (Followers: 7)
Journal of Archival Organization     Hybrid Journal   (Followers: 28)
Journal of Copyright in Education & Librarianship     Open Access   (Followers: 29)
Journal of Creative Library Practice     Open Access   (Followers: 98)
Journal of Data Mining and Digital Humanities     Open Access   (Followers: 39)
Journal of Documentation     Hybrid Journal   (Followers: 161)
Journal of East Asian Libraries     Open Access   (Followers: 7)
Journal of Education in Library and Information Science - JELIS     Full-text available via subscription   (Followers: 71)
Journal of Educational Media & Library Sciences     Open Access   (Followers: 9)
Journal of Educational Media, Memory, and Society     Full-text available via subscription   (Followers: 12)
Journal of Electronic Publishing     Open Access   (Followers: 76)
Journal of Electronic Resources Librarianship     Hybrid Journal   (Followers: 227)
Journal of eScience Librarianship     Open Access   (Followers: 113)
Journal of Global Information Management     Full-text available via subscription   (Followers: 9)
Journal of Health & Medical Informatics     Open Access   (Followers: 50)
Journal of Hospital Librarianship     Hybrid Journal   (Followers: 153)
Journal of Information & Knowledge Management     Hybrid Journal   (Followers: 142)
Journal of Information and Data Management     Open Access   (Followers: 14)
Journal of Information Engineering and Applications     Open Access   (Followers: 10)
Journal of Information Literacy     Open Access   (Followers: 776)
Journal of Information Science     Hybrid Journal   (Followers: 1017)
Journal of Information Studies & Technology     Open Access   (Followers: 1)

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Similar Journals
Journal Cover
Information Retrieval
Journal Prestige (SJR): 0.352
Citation Impact (citeScore): 2
Number of Followers: 190  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1573-7659 - ISSN (Online) 1386-4564
Published by Springer-Verlag Homepage  [2469 journals]
  • Recommendations for item set completion: on the semantics of
           item co-occurrence with data sparsity, input size, and input modalities

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      Abstract: Abstract We address the problem of recommending relevant items to a user in order to “complete” a partial set of already-known items. We consider the two scenarios of citation and subject label recommendation, which resemble different semantics of item co-occurrence: relatedness for co-citations and diversity for subject labels. We assess the influence of the completeness of an already known partial item set on the recommender’s performance. We also investigate data sparsity by imposing a pruning threshold on minimum item occurrence and the influence of using additional metadata. As models, we focus on different autoencoders, which are particularly suited for reconstructing missing items in a set. We extend autoencoders to exploit a multi-modal input of text and structured data. Our experiments on six real-world datasets show that supplying the partial item set as input is usually helpful when item co-occurrence resembles relatedness, while metadata are effective when co-occurrence implies diversity. The simple item co-occurrence model is a strong baseline for citation recommendation but can provide good results also for subject labels. Autoencoders have the capability to exploit additional metadata besides the partial item set as input, and achieve comparable or better performance. For the subject label recommendation task, the title is the most important attribute. Adding more input modalities sometimes even harms the results. In conclusion, it is crucial to consider the semantics of the item co-occurrence for the choice of an appropriate model and carefully decide which metadata to exploit.
      PubDate: 2022-04-04
       
  • Guest editorial: special issue on ECIR 2021

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      PubDate: 2022-04-01
       
  • CEQE to SQET: A study of contextualized embeddings for query expansion

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      Abstract: Abstract In this work, we study recent advances in context-sensitive language models for the task of query expansion. We study the behavior of existing and new approaches for lexical word-based expansion in both unsupervised and supervised contexts. For unsupervised models, we study the behavior of the Contextualized Embeddings for Query Expansion (CEQE) model. We introduce a new model, Supervised Contextualized Query Expansion with Transformers (SQET) that performs expansion as a supervised classification task and leverages context in pseudo-relevant results. We study the behavior of these expansion approaches for the tasks of ad-hoc document and passage retrieval. We conduct experiments combining expansion with probabilistic retrieval models as well as neural document ranking models. We evaluate expansion effectiveness on three standard TREC collections: Robust, Complex Answer Retrieval, and Deep Learning. We analyze the results of extrinsic retrieval effectiveness, intrinsic ability to rank expansion terms, and perform a qualitative analysis of the differences between the methods. We find out CEQE statically significantly outperforms static embeddings across all three datasets for Recall@1000. Moreover, CEQE outperforms static embedding-based expansion methods on multiple collections (by up to 18% on Robust and 31% on Deep Learning on average precision) and also improves over proven probabilistic pseudo-relevance feedback (PRF) models. SQET outperforms CEQE by 6% in P@20 on the intrinsic term ranking evaluation and is approximately as effective in retrieval performance. Models incorporating neural and CEQE-based expansion score achieves gains of up to 5% in P@20 and 2% in AP on Robust over the state-of-the-art transformer-based re-ranking model, Birch.
      PubDate: 2022-03-22
       
  • Open-domain conversational search assistants: the Transformer is all you
           need

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      Abstract: On the quest of providing a more natural interaction between users and search systems, open-domain conversational search assistants have emerged, by assisting users in answering questions about open topics in a conversational manner. In this work, we show how the Transformer architecture achieves state-of-the-art results in key IR tasks, leveraging the creation of conversational assistants that engage in open-domain conversational search with single, yet informative, answers. In particular, we propose a complete open-domain abstractive conversational search agent pipeline to address two major challenges: first, conversation context-aware search and second, abstractive search-answers generation. To address the first challenge, the conversation context is modeled using a query rewriting method that unfolds the context of the conversation up to a specific moment to search for the correct answers. These answers are then passed to a Transformer-based re-ranker to further improve retrieval performance. The second challenge, is tackled with recent ive Transformer architectures to generate a digest of the top most relevant passages. Experiments show that Transformers deliver a solid performance across all tasks in conversational search, outperforming several baselines. This work is an expanded version of Ferreira et al. (Open-domain conversational search assistant with transformers. In: Advances in information retrieval—43rd European conference on IR research, ECIR 2021, virtual event, 28 March–1 April 2021, proceedings, Part I. Springer) which provides more details about the various components of the of the system, and extends the automatic evaluation with a novel user-study, which confirmed the need for the conversational search paradigm, and assessed the performance of our answer generation approach.
      PubDate: 2022-03-14
       
  • CoSearcher: studying the effectiveness of conversational search refinement
           and clarification through user simulation

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      Abstract: Abstract A key application of conversational search is refining a user’s search intent by asking a series of clarification questions, aiming to improve the relevance of search results. Training and evaluating such conversational systems currently requires human participation, making it infeasible to examine a wide range of user behaviors. To support robust training/evaluation of such systems, we propose a simulation framework called CoSearcher Information about code/resources available at https://github.com/amzn/cosearcher that includes a parameterized user simulator controlling key behavioral factors like cooperativeness and patience. To evaluate our approach, we use both a standard conversational query clarification benchmark and develop an extended dataset using query suggestions from a popular Web search engine as a source of additional refinement candidates. Using these datasets, we investigate the impact of a variety of conditions on search refinement and clarification effectiveness over a wide range of user behaviors, semantic policies, and dynamic facet generation. Our results quantify the effects of user behavior variation, and identify conditions required for conversational search refinement and clarification to be effective. This paper is an extended version of our previous work, and includes new experimental results for comparing semantic similarity ranking strategies for facets, using enhanced representations of facets, learning from negative user responses, among other new results and more detailed experimental descriptions.
      PubDate: 2022-03-10
      DOI: 10.1007/s10791-022-09404-z
       
  • sMARE: a new paradigm to evaluate and understand query performance
           prediction methods

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      Abstract: Abstract Query performance prediction (QPP) has been studied extensively in the IR community over the last two decades. A by-product of this research is a methodology to evaluate the effectiveness of QPP techniques. In this paper, we re-examine the existing evaluation methodology commonly used for QPP, and propose a new approach. Our key idea is to model QPP performance as a distribution instead of relying on point estimates. To obtain such distribution, we exploit the scaled Absolute Ranking Error (sARE) measure, and its mean the scaled Mean Absolute Ranking Error (sMARE). Our work demonstrates important statistical implications, and overcomes key limitations imposed by the currently used correlation-based point-estimate evaluation approaches. We also explore the potential benefits of using multiple query formulations and ANalysis Of VAriance (ANOVA) modeling in order to measure interactions between multiple factors. The resulting statistical analysis combined with a novel evaluation framework demonstrates the merits of modeling QPP performance as distributions, and enables detailed statistical ANOVA models for comparative analyses to be created.
      PubDate: 2022-03-07
      DOI: 10.1007/s10791-022-09407-w
       
  • On cross-lingual retrieval with multilingual text encoders

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      Abstract: Abstract Pretrained multilingual text encoders based on neural transformer architectures, such as multilingual BERT (mBERT) and XLM, have recently become a default paradigm for cross-lingual transfer of natural language processing models, rendering cross-lingual word embedding spaces (CLWEs) effectively obsolete. In this work we present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks across a number of diverse language pairs. We first treat these models as multilingual text encoders and benchmark their performance in unsupervised ad-hoc sentence- and document-level CLIR. In contrast to supervised language understanding, our results indicate that for unsupervised document-level CLIR—a setup with no relevance judgments for IR-specific fine-tuning—pretrained multilingual encoders on average fail to significantly outperform earlier models based on CLWEs. For sentence-level retrieval, we do obtain state-of-the-art performance: the peak scores, however, are met by multilingual encoders that have been further specialized, in a supervised fashion, for sentence understanding tasks, rather than using their vanilla ‘off-the-shelf’ variants. Following these results, we introduce localized relevance matching for document-level CLIR, where we independently score a query against document sections. In the second part, we evaluate multilingual encoders fine-tuned in a supervised fashion (i.e., we learn to rank) on English relevance data in a series of zero-shot language and domain transfer CLIR experiments. Our results show that, despite the supervision, and due to the domain and language shift, supervised re-ranking rarely improves the performance of multilingual transformers as unsupervised base rankers. Finally, only with in-domain contrastive fine-tuning (i.e., same domain, only language transfer), we manage to improve the ranking quality. We uncover substantial empirical differences between cross-lingual retrieval results and results of (zero-shot) cross-lingual transfer for monolingual retrieval in target languages, which point to “monolingual overfitting” of retrieval models trained on monolingual (English) data, even if they are based on multilingual transformers.
      PubDate: 2022-03-07
      DOI: 10.1007/s10791-022-09406-x
       
  • Kernel density estimation based factored relevance model for
           multi-contextual point-of-interest recommendation

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      Abstract: Abstract An automated contextual suggestion algorithm is likely to recommend contextually appropriate and personalized ‘points-of-interest’ (POIs) to a user, if it can extract information from the user’s preference history (exploitation) and effectively blend it with the user’s current contextual information (exploration) to predict a POI’s ‘appropriateness’ in the current context. To balance this trade-off between exploitation and exploration, we propose an unsupervised, generic framework involving a factored relevance model (FRLM), constituting two distinct components, one pertaining to historical contexts, and the other corresponding to the current context. We further generalize the proposed FRLM by incorporating the semantic relationships between terms in POI descriptors using kernel density estimation (KDE) on embedded word vectors. Additionally, we show that trip-qualifiers, (e.g. ‘trip-type’, ‘accompanied-by’) are potentially useful information sources that could be used to improve the recommendation effectiveness. Using such information is not straightforward since users’ texts/reviews of visited POIs typically do not explicitly contain such annotations. We undertake a weakly supervised approach to predict the associations between the review-texts in a user profile and the likely trip contexts. Our experiments, conducted on the TREC Contextual Suggestion 2016 dataset, demonstrate that factorization, KDE-based generalizations, and trip-qualifier enriched contexts of the relevance model improve POI recommendation.
      PubDate: 2022-01-21
      DOI: 10.1007/s10791-021-09400-9
       
  • Efficient query processing techniques for next-page retrieval

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      Abstract: Abstract In top-k ranked retrieval the goal is to efficiently compute an ordered list of the highest scoring k documents according to some stipulated similarity function such as the well-known BM25 approach. In most implementation techniques a min-heap of size k is used to track the top scoring candidates. In this work we consider the question of how best to retrieve the second page of search results, given that a first page has already been computed; that is, identification of the documents at ranks \(k+1\) to 2k for some query. Our goal is to understand what information is available as a by-product of the first-page scoring, and how it can be employed to accelerate the second-page computation, assuming that the second-page of results is required for only a fraction of the query load. We propose a range of simple, yet efficient, next-page retrieval techniques which are suitable for accelerating Document-at-a-Time mechanisms, and demonstrate their performance on three large text collections.
      PubDate: 2022-01-18
      DOI: 10.1007/s10791-021-09402-7
       
  • Measurement of clustering effectiveness for document collections

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      Abstract: Abstract Clustering of the contents of a document corpus is used to create sub-corpora with the intention that they are expected to consist of documents that are related to each other. However, while clustering is used in a variety of ways in document applications such as information retrieval, and a range of methods have been applied to the task, there has been relatively little exploration of how well it works in practice. Indeed, given the high dimensionality of the data it is possible that clustering may not always produce meaningful outcomes. In this paper we use a well-known clustering method to explore a variety of techniques, existing and novel, to measure clustering effectiveness. Results with our new, extrinsic techniques based on relevance judgements or retrieved documents demonstrate that retrieval-based information can be used to assess the quality of clustering, and also show that clustering can succeed to some extent at gathering together similar material. Further, they show that intrinsic clustering techniques that have been shown to be informative in other domains do not work for information retrieval. Whether clustering is sufficiently effective to have a significant impact on practical retrieval is unclear, but as the results show our measurement techniques can effectively distinguish between clustering methods.
      PubDate: 2022-01-10
      DOI: 10.1007/s10791-021-09401-8
       
  • Search results diversification for effective fair ranking in academic
           search

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      Abstract: Abstract Providing users with relevant search results has been the primary focus of information retrieval research. However, focusing on relevance alone can lead to undesirable side effects. For example, small differences between the relevance scores of documents that are ranked by relevance alone can result in large differences in the exposure that the authors of relevant documents receive, i.e., the likelihood that the documents will be seen by searchers. Therefore, developing fair ranking techniques to try to ensure that search results are not dominated, for example, by certain information sources is of growing interest, to mitigate against such biases. In this work, we argue that generating fair rankings can be cast as a search results diversification problem across a number of assumed fairness groups, where groups can represent the demographics or other characteristics of information sources. In the context of academic search, as in the TREC Fair Ranking Track, which aims to be fair to unknown groups of authors, we evaluate three well-known search results diversification approaches from the literature to generate rankings that are fair to multiple assumed fairness groups, e.g. early-career researchers vs. highly-experienced authors. Our experiments on the 2019 and 2020 TREC datasets show that explicit search results diversification is a viable approach for generating effective rankings that are fair to information sources. In particular, we show that building on xQuAD diversification as a fairness component can result in a significant ( \(p<0.05\) ) increase (up to  50% in our experiments) in the fairness of exposure that authors from unknown protected groups receive.
      PubDate: 2021-12-07
      DOI: 10.1007/s10791-021-09399-z
       
  • Neural ranking models for document retrieval

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      Abstract: Abstract Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-crafted features. Recently, researchers have leveraged deep learning models in information retrieval. These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations of hand-crafted features. A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking. In this paper, we compare the proposed models in the literature along different dimensions in order to understand the major contributions and limitations of each model. In our discussion of the literature, we analyze the promising neural components, and propose future research directions. We also show the analogy between document retrieval and other retrieval tasks where the items to be ranked are structured documents, answers, images and videos.
      PubDate: 2021-12-01
      DOI: 10.1007/s10791-021-09398-0
       
  • Combining semi-supervised and active learning to rank algorithms:
           application to Document Retrieval

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      Abstract: Abstract Generally, the purpose of learning to rank methods is to combine the results from existing ranking models that within a single ranking function, applied to order the documents as efficiently as possible, improving the quality lists of results returned. However, learning to rank has several limitations namely the creation and size of the labeled database. We have considered the two frameworks of semi-supervised and active learning in order to look for solutions to these problems. We have been interested in semi-supervised, active and semi-active learning to rank algorithms for Document Retrieval (DR) which is a ranking application of alternatives. A good balance between exploration and exploitation has a positive impact on the performance of the learning. Thus, we have focused firstly on two active learning to rank algorithms that use supervised learning and semi-supervised learning as auxiliaries and use an automatic method for the labeling of unlabeled pairs selected. These algorithms are named “Semi-Active Learning to Rank: SAL2R” and “Active-Semi-Supervised Learning to Rank: ASSL2R”. We have been particulary interested in providing efficient and effective algorithms to handle a large set of unlabeled data. Second, we have considered improvement of these semi-active SAL2R and ASSL2R algorithms using a multi-pair in the selection step. Our contribution lies particulary in the in depth experimental study of the performance of these algorithms and precisely the influence of certain fixed parameters on the learned ranking function.
      PubDate: 2021-12-01
      DOI: 10.1007/s10791-021-09396-2
       
  • Variational Bayesian representation learning for grocery recommendation

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      Abstract: Abstract Representation learning has been widely applied in real-world recommendation systems to capture the features of both users and items. Existing grocery recommendation methods only represent each user and item by single deterministic points in a low-dimensional continuous space, which limit the expressive ability of their embeddings, resulting in recommendation performance bottlenecks. In addition, existing representation learning methods for grocery recommendation only consider the items (products) as independent entities, neglecting their other valuable side information, such as the textual descriptions and the categorical data of items. In this paper, we propose the Variational Bayesian Context-Aware Representation (VBCAR) model for grocery recommendation. VBCAR is a novel variational Bayesian model that learns distributional representations of users and items by leveraging basket context information from historical interactions. Our VBCAR model is also extendable to leverage side information by encoding contextual features into representations based on the inference encoder. We conduct extensive experiments on three real-world grocery datasets to assess the effectiveness of our model as well as the impact of different construction strategies for item side information. Our results show that our VBCAR model outperforms the current state-of-the-art grocery recommendation models while integrating item side information (especially the categorical features with the textual information of items) results in further significant performance gains. Furthermore, we demonstrate through analysis that our model is able to effectively encode similarities between product types, which we argue is the primary reason for the observed effectiveness gains.
      PubDate: 2021-10-01
      DOI: 10.1007/s10791-021-09397-1
       
  • Strong natural language query generation

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      Abstract: In this paper, we propose a novel query generation task we refer to as the Strong Natural Language Query (SNLQ) problem. The key idea we explore is how to best learn document summarization and ranker effectiveness jointly in order to generate human-readable queries which capture the information need conveyed by a document, and that can also be used for refinding tasks and query rewriting. Our problem is closely related to two well-known retrieval problems—known-item finding and strong query generation—with the additional objective of maximizing query informativeness. In order to achieve this goal, we combine state-of-the-art abstractive summarization techniques and reinforcement learning. We have empirically compared our new approaches with several closely related baselines using the MS-MARCO data collection, and show that the approach is capable of achieving substantially better trade-off between effectiveness and human-readability than have been reported previously.
      PubDate: 2021-10-01
      DOI: 10.1007/s10791-021-09395-3
       
  • Pseudo relevance feedback optimization

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      Abstract: Abstract We propose a method for automatic optimization of pseudo relevance feedback (PRF) in information retrieval. Based on the conjecture that the initial query’s contribution to the final query may not be necessary once a good model is built from pseudo relevant documents, we set out to optimize per query only the number of top-retrieved documents to be used for feedback. The optimization is based on several query performance predictors for the initial query, by building a linear regression model discovering the optimal machine learning pipeline via genetic programming. Even by using only 50–100 training queries, the method yields statistically-significant improvements in MAP of 18–35% over the initial query, 7–11% over the feedback model with the best fixed number of pseudo-relevant documents, and up to 10% (5.5% on median) over the standard method of optimizing both the balance coefficient and the number of feedback documents by grid-search in the training set. Compared to state-of-the-art PRF methods from the recent literature, our method outperforms by up to 21% with an average of 10%. Further analysis shows that we are still far from the method’s effectiveness ceiling (in contrast to the standard method), leaving amble room for further improvements.
      PubDate: 2021-10-01
      DOI: 10.1007/s10791-021-09393-5
       
  • Using word semantic concepts for plagiarism detection in text documents

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      Abstract: Abstract Plagiarism is a common problem in the modern age. With the advance of Internet, it is more and more convenient to access other people’s writings or publications. When someone uses the content of a text in an undesirable way, plagiarism may occur. Plagiarism infringes the intellectual property rights, so it is a serious problem nowadays. However, detecting plagiarism effectively is a challenging work. Traditional methods, like vector space model or bag-of-words, are short of providing a good solution due to the incapability of handling the semantics of words satisfactorily. In this paper, we propose a new method for plagiarism detection. We use Word2vec to transform the words into word vectors which are able to reveal the semantic relationship among different words. Through word vectors, words are clustered into concepts. Then documents and their paragraphs are represented in terms of concepts, and plagiarism detection can be done more effectively. A number of experiments are conducted to demonstrate the good performance of our proposed method.
      PubDate: 2021-10-01
      DOI: 10.1007/s10791-021-09394-4
       
  • Algorithmic copywriting: automated generation of health-related
           advertisements to improve their performance

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      Abstract: Abstract Search advertising, a popular method for online marketing, has been employed to improve health by eliciting positive behavioral change. However, writing effective advertisements requires expertise and experimentation, which may not be available to health authorities wishing to elicit such changes, especially when dealing with public health crises such as epidemic outbreaks. Here, we develop a framework, comprising two neural network models, that automatically generates ads. The framework first employs a generator model, which creates ads from web pages. These ads are then processed by a translation model, which transcribes ads to improve performance. We trained the networks using 114K health-related ads shown on Microsoft Advertising. We measure ad performance using the click-through rates (CTR). Our experiments show that the generated advertisements received approximately the same CTR as human-authored ads. The marginal contribution of the generator model was, on average, 28% lower than that of human-authored ads, while the translator model received, on average, 32% more clicks than human-authored ads. Our analysis shows that, when compared to human-authored ads, both the translator model and the combined generator + translator framework produce ads reflecting higher values of psychological attributes associated with a user action, including higher valence and arousal, and more calls to action. In contrast, levels of these attributes in ads produced by the generator model alone are similar to those of human-authored ads. Our results demonstrate the ability to automatically generate useful advertisements for the health domain. We believe that our work offers health authorities an improved ability to build effective public health advertising campaigns.
      PubDate: 2021-06-01
      DOI: 10.1007/s10791-021-09392-6
       
  • Improved reviewer assignment based on both word and semantic features

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      Abstract: Abstract Assigning appropriate reviewers to a manuscript from a pool of candidate reviewers is a common challenge in the academic community. Current word- and semantic-based approaches treat the reviewer assignment problem (RAP) as an information retrieval problem but do not take into account two constraints of the RAP: incompleteness of the reviewer data and interference from nonmanuscript-related papers. In this paper, a word and semantic-based iterative model (WSIM) is proposed to account for the constraints of the RAP by improving the similarity calculations between reviewers and manuscripts. First, we use the improved language model and topic model to extract word features and semantic features to represent reviewers and manuscripts. Second, we use a similarity metric based on the normalized discounted cumulative gain (NDCG) to measure semantic similarity. This metric ignores the probability value (quantitative exact value) of the topic and considers only the ranking (qualitative relevance), thus reducing overfitting to incomplete reviewer data. Finally, we use an iterative model to reduce the interference from nonmanuscript-related papers in the reviewer data. This approach considers the similarity between the manuscript and each of the reviewer’s papers. We evaluate the proposed WSIM on two real datasets and compare its performance to that of seven existing methods. The experimental results show that the WSIM improves the recommendation accuracy by at least 2.5% on the top 20.
      PubDate: 2021-06-01
      DOI: 10.1007/s10791-021-09390-8
       
  • Topic-independent modeling of user knowledge in informational search
           sessions

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      Abstract: Abstract Web search is among the most frequent online activities. In this context, widespread informational queries entail user intentions to obtain knowledge with respect to a particular topic or domain. To serve learning needs better, recent research in the field of interactive information retrieval has advocated the importance of moving beyond relevance ranking of search results and considering a user’s knowledge state within learning oriented search sessions. Prior work has investigated the use of supervised models to predict a user’s knowledge gain and knowledge state from user interactions during a search session. However, the characteristics of the resources that a user interacts with have neither been sufficiently explored, nor exploited in this task. In this work, we introduce a novel set of resource-centric features and demonstrate their capacity to significantly improve supervised models for the task of predicting knowledge gain and knowledge state of users in Web search sessions. We make important contributions, given that reliable training data for such tasks is sparse and costly to obtain. We introduce various feature selection strategies geared towards selecting a limited subset of effective and generalizable features.
      PubDate: 2021-06-01
      DOI: 10.1007/s10791-021-09391-7
       
 
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