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Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering

Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering,10.1145/963770.963775,ACM Transactions on Infor

Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering   (Citations: 177)
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Journal: ACM Transactions on Information Systems - TOIS , vol. 22, no. 1, pp. 116-142, 2004
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    • ...These two properties, known as propagation and attenuation [26, 34], are often observed in graph-based similarity measures...
    • ...In the first approach, the proximity of a user u to an item i in the graph is used directly to evaluate the rating of u for i [19, 26, 34]...
    • ...The number of paths between a user and an item in a bipartite graph can also be used to evaluate their compatibility [34]...
    • ...To overcome these limitations, spreading activation techniques [14] have been used in [34]...

    Christian Desrosierset al. A Comprehensive Survey of Neighborhood-based Recommendation Methods

    • ...As mentioned in [10], one of the most commonly-used and successfully-deployed recommendation approaches is collaborative filtering...

    Hao Maet al. Recommender systems with social regularization

    • ...Researchers suggest recommendation techniques to resolve this problem [6, 8, 13]...
    • ...If we recommend an item using neighbors computed from a small amount of ratings, then the accuracy of recommendation is lower than using neighbors computed from a large amount of ratings [2, 9]. The sparsity problem affects the accuracy of recommendation using collaborative filtering [6, 8, 13]...
    • ...Researchers proposed several methods to avoid this problem [3, 6, 7, 8, 11, 13]...

    Bernhard Scholzet al. Analyzing category correlations for recommendation system

    • ...Based on the approach of [18], [16], a more realistic evaluation of recommendation should consider the division of tags/items of each test user into two sets: 1) the past tags/ items of the test user, and 2) the future tags/items of the test user...

    Panagiotis Symeonidiset al. A Unified Framework for Providing Recommendations in Social Tagging Sy...

    • ...However, in practical recommender systems, insufficient item rating data are a critical problem, which refers to the lack of prior transactional and feedback data that makes it difficult and unreliable to predict which users are similar to a given user [10]...

    Justin Zhanet al. Privacy-Preserving Collaborative Recommender Systems

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