Abstract
Recently, there has been a growing need for more sophisticated recommendation techniques with an increase in the amount of data available on the Web. In this study, we especially focus on recommending items with long text, and aim at achieving this using a method of link analysis of a user-item bipartite graph in a regularization framework based on Co-HITS algorithm. This method can integrate, via mutual reinforcement, the graph structure and the content of both user profiles and items. It has never been seen in the mainstream of conventional recommendation techniques. In our experiments, we used the data of Web browsing history, assuming Web news articles as target items. We evaluated the list of top-N items recommended based on the browsing history, using a test set that consists of a part of viewed items for each user. We demonstrate through the experiments that the proposed method outperformed several baseline methods in a situation where only a small amount of browsing behavior is observed.
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Naito, S., Eguchi, K. (2012). Robust Recommendations Using Regularized Link Analysis of Browsing Behavior Graphs. In: Yang, S.J., Greenberg, A.M., Endsley, M. (eds) Social Computing, Behavioral - Cultural Modeling and Prediction. SBP 2012. Lecture Notes in Computer Science, vol 7227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29047-3_41
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DOI: https://doi.org/10.1007/978-3-642-29047-3_41
Publisher Name: Springer, Berlin, Heidelberg
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