Adapting vector space model to ranking-based collaborative filtering

Authors:
Shuaiqiang Wang;Jiankai Sun;Byron J. Gao;Jun Ma
Affiliations:
Shandong University of Finance and Economics, Jinan, China;Shandong University, Shandong University, China;Texas State University-San Marcos, San Marcos, TX, USA;Shandong University, Jinan, China
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

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Abstract

Collaborative filtering (CF) is an effective technique addressing the information overload problem. Recently ranking-based CF methods have shown advantages in recommendation accuracy, being able to capture the preference similarity between users even if their rating scores differ significantly. In this study, we seek accuracy improvement of ranking-based CF through adaptation of the vector space model, where we consider each user as a document and her pairwise relative preferences as terms. We then use a novel degree-specialty weighting scheme resembling TF-IDF to weight the terms. Then we use cosine similarity to select a neighborhood of users for the target user to make recommendations. Experiments on benchmarks in comparison with the state-of-the-art methods demonstrate the promise of our approach.