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Journal Cover Journal of Information Science
   [781 followers]  Follow    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
     ISSN (Print) 0165-5515 - ISSN (Online) 1741-6485
     Published by Sage Publications Homepage  [743 journals]   [SJR: 1.199]   [H-I: 35]
  • MapReduce-based web mining for prediction of web-user navigation
    • Authors: Li, M; Yu, X, Ryu, K. H.
      Pages: 557 - 567
      Abstract: Predicting web user behaviour is typically an application for finding frequent sequence patterns. With the rapid growth of the Internet, a large amount of information is stored in web logs. Traditional frequent-sequence-pattern-mining algorithms are hard pressed to analyse information from within big datasets. In this paper, we propose an efficient way to predict navigation patterns of web users by improving frequent-sequence-pattern-mining algorithms based on the programming model of MapReduce, which can handle huge datasets efficiently. During the experiments, we show that our proposed MapReduce-based algorithm is more efficient than traditional frequent-sequence-pattern-mining algorithms, and by comparing our proposed algorithms with current existed algorithms in web-usage mining, we also prove that using the MapReduce programming model saves time.
      PubDate: 2014-09-12T03:14:15-07:00
      DOI: 10.1177/0165551514544096|hwp:master-id:spjis;0165551514544096
      Issue No: Vol. 40, No. 5 (2014)
       
  • A continuous rating model for news recommendation
    • Authors: Gao, H; Chen, D.-B, Wang, G.-N, Mensah, D. N. A, Fu, Y.
      Pages: 568 - 577
      Abstract: With the explosive growth of online news, adaptive news recommendation models based on social connections have developed as a result of the growth of online news websites. Several models have relayed better outcomes for users. Most of these models establish users' social connections according to their binary news rating scores. In various real systems, mainly based on a non-binary pattern, the rating score is often multigrade. In this paper, we introduce a new continuous ratings model for news recommendations. This model uses exponential distance to measure the similarities of users to increase its robustness to noise. Simulation results show that the proposed model has significantly improved the performance of personalized news recommendation.
      PubDate: 2014-09-12T03:14:15-07:00
      DOI: 10.1177/0165551514542065|hwp:master-id:spjis;0165551514542065
      Issue No: Vol. 40, No. 5 (2014)
       
  • Detecting hot topics from Twitter: A multiview approach
    • Authors: Fang, Y; Zhang, H, Ye, Y, Li, X.
      Pages: 578 - 593
      Abstract: Twitter is widely used all over the world, and a huge number of hot topics are generated by Twitter users in real time. These topics are able to reflect almost every aspect of people’s daily lives. Therefore, the detection of topics in Twitter can be used in many real applications, such as monitoring public opinion, hot product recommendation and incidence detection. However, the performance of traditional topic detection methods is still far from perfect largely owing to the tweets’ features, such as their limited length and arbitrary abbreviations. To address these problems, we propose a novel framework (MVTD) for Twitter topic detection using multiview clustering, which can integrate multirelations among tweets, such as semantic relations, social tag relations and temporal relations. We also propose some methods for measuring relations among tweets. In particular, to better measure the semantic similarity of tweets, we propose a new document similarity measure based on a suffix tree (STVSM). In addition, a new keyword extraction method based on a suffix tree is proposed. Experiments on real datasets show that the performance of MVTD is much better than that of a single view, and it is useful for detecting topics from Twitter.
      PubDate: 2014-09-12T03:14:15-07:00
      DOI: 10.1177/0165551514541614|hwp:master-id:spjis;0165551514541614
      Issue No: Vol. 40, No. 5 (2014)
       
  • Folksonomy-based user interest and disinterest profiling for improved
           recommendations: An ontological approach
    • Authors: Movahedian, H; Khayyambashi, M. R.
      Pages: 594 - 610
      Abstract: Social tagging has revolutionized the social and personal experience of users across numerous web platforms by enabling the organizing, managing, sharing and searching of web data. The extensive amount of information generated by tagging systems can be utilized for recommendation purposes. However, the unregulated creation of social tags by users can produce a great deal of noise and the tags can be unreliable; thus, exploiting them for recommendation is a nontrivial task. In this study, a new recommender system is proposed based on the similarities between user and item profiles. The approach applied is to generate user and item profiles by discovering tag patterns that are frequently generated by users. These tag patterns are categorized into irrelevant patterns and relevant patterns which represent diverse user preferences in terms of likes and dislikes. Furthermore, presented here is a method for translating these tag-based profiles into semantic profiles by determining the underlying meaning(s) of the tags, and mapping them to semantic entities belonging to external knowledge bases. To alleviate the cold start and overspecialization problems, semantic profiles are enriched in two phases: (a) using a semantic spreading mechanism and then (b) inheriting the preferences of similar users. Experiment indicates that this approach not only provides a better representation of user interests, but also achieves a better recommendation result when compared with existing methods. The performance of the proposed recommendation method is investigated in the face of the cold start problem, the results of which confirm that it can indeed remedy the problem for early adopters, hence improving overall recommendation quality.
      PubDate: 2014-09-12T03:14:15-07:00
      DOI: 10.1177/0165551514539870|hwp:master-id:spjis;0165551514539870
      Issue No: Vol. 40, No. 5 (2014)
       
  • Topic evolution based on LDA and HMM and its application in stem cell
           research
    • Authors: Wu, Q; Zhang, C, Hong, Q, Chen, L.
      Pages: 611 - 620
      Abstract: This paper analyses topic segmentation based on the LDA (Latent Dirichlet Allocation) model, and performs the topic segmentation and topic evolution of stem cell research literatures in PubMed from 2001 to 2012 by combining the HMM (Hidden Markov Model) and co-occurrence theory. Stem cell research topics were obtained with LDA and expert judgements made on these topics to test the feasibility of the model classification. Further, the correlation between topics was analysed. HMM was used to predict the trend evolution of topics over various years, and a time series map was used to visualize the evolutional relationships among the stem cell topics.
      PubDate: 2014-09-12T03:14:15-07:00
      DOI: 10.1177/0165551514540565|hwp:master-id:spjis;0165551514540565
      Issue No: Vol. 40, No. 5 (2014)
       
  • ADM-LDA: An aspect detection model based on topic modelling using the
           structure of review sentences
    • Authors: Bagheri, A; Saraee, M, de Jong, F.
      Pages: 621 - 636
      Abstract: Probabilistic topic models are statistical methods whose aim is to discover the latent structure in a large collection of documents. The intuition behind topic models is that, by generating documents by latent topics, the word distribution for each topic can be modelled and the prior distribution over the topic learned. In this paper we propose to apply this concept by modelling the topics of sentences for the aspect detection problem in review documents in order to improve sentiment analysis systems. Aspect detection in sentiment analysis helps customers effectively navigate into detailed information about their features of interest. The proposed approach assumes that the aspects of words in a sentence form a Markov chain. The novelty of the model is the extraction of multiword aspects from text data while relaxing the bag-of-words assumption. Experimental results show that the model is indeed able to perform the task significantly better when compared with standard topic models.
      PubDate: 2014-09-12T03:14:15-07:00
      DOI: 10.1177/0165551514538744|hwp:master-id:spjis;0165551514538744
      Issue No: Vol. 40, No. 5 (2014)
       
  • An empirical study on the evaluation of interlinking tools on the Web of
           Data
    • Authors: Rajabi, E; Sicilia, M.-A, Sanchez-Alonso, S.
      Pages: 637 - 648
      Abstract: The rise and widespread use of Linked Data has encouraged data providers to publish and link their content in order to classify and organize information in a useful fashion. Interlinking between datasets enhances data navigation and facilitates searching. As a result, the use of interlinking tools as a way of connecting data items to the Linked Open Data cloud has become more prevalent. In this paper, we examine the results obtained by three interlinking tools used to link a large educational collection to the Linked Open Data datasets. The generated output by the interlinking tools, which was later assessed by human experts, illustrates that data publishers can rely on current interlinking approaches and thus adopt them to connect their resources to the Web of Data. Our findings also provide evidence that two of these tools, namely Silk and LIMES, can be considered as the most promising.
      PubDate: 2014-09-12T03:14:15-07:00
      DOI: 10.1177/0165551514538151|hwp:master-id:spjis;0165551514538151
      Issue No: Vol. 40, No. 5 (2014)
       
  • Exploring the determinants of cross-boundary information sharing in the
           public sector: An e-Government case study in Taiwan
    • Authors: Yang, T.-M; Wu, Y.-J.
      Pages: 649 - 668
      Abstract: This paper explores the complexity of cross-boundary information sharing in the public sector. In particular, determinants influencing interagency information sharing are investigated and discussed, and a case study of Taiwan e-Government is employed. Four perspectives, including technology, organization, legislation and policy, and environment, are used to conduct this exploratory inquiry. Legislation and policy is found to be the most influential factor among government agencies participating in information-sharing initiatives. Organizational factors are also found to be significant, but less so than legislation and policy. Technological factors are considered relatively more easily addressed when compared with legislation and policy and organizational factors. Finally, situational factors are also found to have respective impacts on interagency information sharing. In addition to factors that are discussed in the current literature, newly identified factors are illustrated to provide insights. Moreover, in order to better conceptualize how identified factors determine agencies’ intentions towards cross-boundary information sharing, theory of planned behaviour is used to form a theoretical discussion by integrating the identified factors of the study. Practical implications are also provided to address how cross-boundary information sharing among government agencies can be better achieved. Lastly, the conclusion outlines the contributions and limitations of this research and suggests future studies related to the current work.
      PubDate: 2014-09-12T03:14:15-07:00
      DOI: 10.1177/0165551514538742|hwp:master-id:spjis;0165551514538742
      Issue No: Vol. 40, No. 5 (2014)
       
  • Modalities, motivations, and materials - investigating traditional and
           social online Q&A services
    • Authors: Shah, C; Kitzie, V, Choi, E.
      Pages: 669 - 687
      Abstract: With the advent of ubiquitous connectivity and a constant flux of user-generated content, people’s online information-seeking behaviours are rapidly changing, one o f which includes seeking information from peers through online questioning. Ways to understand this new behaviour can be broken down into three aspects, also referred to as the three M’s – the modalities (sources and strategies) that people use when asking their questions online, their motivations behind asking these questions and choosing specific services, and the types and quality of the materials (content) generated in such an online Q&A environment. This article will provide a new framework – three M’s – based on the synthesis of relevant literature. It will then identify some of the gaps in our knowledge about online Q&A based on this framework. These gaps will be transformed into six research questions, stemming from the three M’s, and addressed by (a) consolidating and synthesizing findings previously reported in the literature, (b) conducting new analyses of data used in prior work, and (c) administering a new study to answer questions unaddressed by the pre-existing and new analyses of prior work.
      PubDate: 2014-09-12T03:14:15-07:00
      DOI: 10.1177/0165551514534140|hwp:master-id:spjis;0165551514534140
      Issue No: Vol. 40, No. 5 (2014)
       
  • External methods to address limitations of using global information on the
           narrow-down approach for hierarchical text classification
    • Authors: Oh, H.-S; Jung, Y.
      Pages: 688 - 708
      Abstract: Classifying documents to a large-scale web taxonomy is a challenging research problem because of a large number of categories and associated documents in the taxonomy. The state-of-the-art solution known as the narrow-down approach utilizes a search engine to reduce an entire category hierarchy to most relevant categories and selects the best one among them using a classifier. In a recent language modelling approach, top-level category information (or global information) was used in judging the appropriateness of a local category, which led to performance improvements. However, we observe that using global information has a limited influence on the final category selection under some conditions. First, global information may be inaccurate even though it is generated by a top-level category classifier using an entire hierarchy. Second, it has little influence when two competing categories share the same top-level category or when the local category information has too strong an influence on the final category selection. To resolve the limitations, in this paper, we propose two external methods: (1) a meta-classifier with novel dependency features among top-level categories based on an ensemble learning framework; and (2) a query modification model based on a statistical feedback method to improve the query document representation instead of just juggling with information in the hierarchy. Our methods were evaluated using the Open Directory Project test collection.
      PubDate: 2014-09-12T03:14:15-07:00
      DOI: 10.1177/0165551514544626|hwp:master-id:spjis;0165551514544626
      Issue No: Vol. 40, No. 5 (2014)
       
 
 
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