Abstract：Most ranking-oriented collaborative filtering (CF) algorithms have two limitations. Firstly, they only consider the accordance of user preferences but ignore the degrees and popularities of user preferences when computing user similarities. Secondly, an intermediate step is necessary to formulate the value function, for preference prediction and aggregation in greedy algorithms to derive recommendation lists. To address these problems, we propose a Degree-Popularity weighting scheme integrating TF-IDF to weight the degrees and popularities of user pairwise relative preferences, and compute similarities between users based on weighted Kendall Tau rank correlation coefficient. Preference aggregations and predictions are directly formulated and the recommendation lists are consequently derived by applying the Schulze method. We conduct extensive experiments on two movie datasets under NDCG evaluation, implying advantageous results in comparison with the state-of-the-art CF algorithms.
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2014, Vol. 28 
