260 research outputs found

    Collaborative Filtering in Social Tagging Systems Based on Joint Item-Tag Recommendations

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    Tapping into the wisdom of the crowd, social tagging can be considered an alternative mechanism - as opposed to Web search - for organizing and discovering information on the Web. Effective tag-based recommendation of information items, such as Web resources, is a critical aspect of this social information discovery mechanism. A precise understanding of the information structure of social tagging systems lies at the core of an effective tag-based recommendation method. While most of the existing research either implicitly or explicitly assumes a simple tripartite graph structure for this purpose, we propose a comprehensive information structure to capture all types of co-occurrence information in the tagging data. Based on the proposed information structure, we further propose a unified user profiling scheme to make full use of all available information. Finally, supported by our proposed user profile, we propose a novel framework for collaborative filtering in social tagging systems. In our proposed framework, we first generate joint item-tag recommendations, with tags indicating topical interests of users in target items. These joint recommendations are then refined by the wisdom from the crowd and projected to the item space for final item recommendations. Evaluation using three real-world datasets shows that our proposed recommendation approach significantly outperformed state-of-the-art approaches

    A Random Walk Model for Item Recommendation in Social Tagging Systems

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    Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of the ternary interaction data among users, items, and tags limits the performance of tag-based recommendation algorithms. In this article, we propose to deal with the sparsity problem in social tagging by applying random walks on ternary interaction graphs to explore transitive associations between users and items. The transitive associations in this article refer to the path of the link between any two nodes whose length is greater than one. Taking advantage of these transitive associations can allow more accurate measurement of the relevance between two entities (e.g., user-item, user-user, and item-item). A PageRank-like algorithm has been developed to explore these transitive associations by spreading users\u27 preferences on an item similarity graph and spreading items\u27 influences on a user similarity graph. Empirical evaluation on three real-world datasets demonstrates that our approach can effectively alleviate the sparsity problem and improve the quality of item recommendation

    Improving Users' Acceptance in Recommender System

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    Ph.DDOCTOR OF PHILOSOPH

    Recommender Systems

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    The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

    An efficient tagging data interpretation and representation scheme for item recommendation

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    A tag-based item recommendation method generates an ordered list of items, likely interesting to a particular user, using the users past tagging behaviour. However, the users tagging behaviour varies in different tagging systems. A potential problem in generating quality recommendation is how to build user profiles, that interprets user behaviour to be effectively used, in recommendation models. Generally, the recommendation methods are made to work with specific types of user profiles, and may not work well with different datasets. In this paper, we investigate several tagging data interpretation and representation schemes that can lead to building an effective user profile. We discuss the various benefits a scheme brings to a recommendation method by highlighting the representative features of user tagging behaviours on a specific dataset. Empirical analysis shows that each interpretation scheme forms a distinct data representation which eventually affects the recommendation result. Results on various datasets show that an interpretation scheme should be selected based on the dominant usage in the tagging data (i.e. either higher amount of tags or higher amount of items present). The usage represents the characteristic of user tagging behaviour in the system. The results also demonstrate how the scheme is able to address the cold-start user problem

    CUSTOMER REVIEWS ANALYSIS WITH DEEP NEURAL NETWORKS FOR E-COMMERCE RECOMMENDER SYSTEMS

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    The first part of this thesis systematically reviews the trend of researches conducted from 2011 to 2018 in terms of challenges and problems regarding developing a recommendation system, areas of application, proposed methodologies, evaluations criteria used to assess the performance and limitations and drawbacks that require investigation and improvements. The study provides an overview for those who are interested in this field to understand the current and the future research opportunities. The second part of this thesis proposes a new methodology to consider customer reviews in recommender systems. An essential prerequisite of an effective recommender system is providing helpful information regarding users and items to generate high-quality recommendations. Customer reviews are a rich source of information that can offer insights into the recommender systems. However, dealing with the customer feedback in text format, as unstructured data, is challenging. Our research includes extraction of the features from customer reviews and use them for similarity evaluation of the users to generate the recommendations. To do so, we have developed a glossary of features for each product category using Latent Dirichlet Allocation. We then employed a deep neural network to extract deep features from the users-attributes matrix to deal with sparsity, ambiguity, and redundancy. Furthermore, we then applied matrix factorization as the collaborative filtering method to provide recommendations. The experimental results using Amazon dataset demonstrate that our methodology improves the performance of the recommender system by incorporating information from reviews when compared to the baselines

    User and document group approach of clustering in tagging systems

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    In this paper, we propose a spectral clustering approach for users and documents group modeling in order to capture the common preference and relatedness of users and documents, and to reduce the time complexity of similarity calculations. In experiments, we investigate the selection of the optimal amount of clusters. We also show a reduction of the time consuming in calculating the similarity for the recommender systems by selecting a centroid first, and then compare the inside item on behalf of each group. keywords: User Profile, Document Profile, Spectral Clustering, Group Profile, Modularity Metric
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