Adaptive Kendall's Τ Correlation in Bipartite Network for Recommendation

The commonly used algorithms in recommender system tend to recommend popular items. The recently proposed algorithm, denoted as G-CosRA, shows good performance in handling this problem, with two parameters to control the popularity of items and activeness of users. In this paper, we refine this algorithm and propose a new recommendation algorithm based on adaptive Kendall’s τ correlation, where only one tuning parameter is involved. The proposal has better performance in accuracy, popularity and diversity, compared with G-CosRA and other existing algorithms. A parameter-free version, named weighted Kendall, is also proposed for better efficiency in computing.

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