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Div-clustering: Exploring active users for social collaborative recommendation
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DOI：10.1016/j.jnca.2013.02.016.png)
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Collaborative recommendation (CR) is a popular method of filtering items that may interest social users by referring to the opinions of friends and acquaintances in the network and computer applications. However, CR involves a cold-start problem, in which a newly established recommender system usually exhibits low recommending accuracy because of insufficient data, such as lack of ratings from users. In this study, we rigorously identify active users in social networks, who are likely to share and accept a recommendation in each data cluster to enhance the performance of the recommendation system and solve the cold-start problem. This novel modified CR method called div-clustering is presented to cluster Web entities in which the properties are specified formally in a recommendation framework, with the reusability of the user modeling component considered. We improve the traditional k-means clustering algorithm by applying supplementary works such as compensating for nominal values supported by the knowledge base, as well as computing and updating the k value. We use the data from two different cases to test for accuracy and demonstrate high quality in div-clustering against a baseline CR algorithm. The experimental results of both offline and online evaluations, which also consider in detail the volunteer profiles, indicate that the CR system with div-clustering obtains more accurate results than does the baseline system. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
Collaborative recommendation
Social network
Div-clustering
Active users
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