A brief review of the past researches on CF shows that methods for calculating users' similarities are almost Pearson Correlation or (adjusted) Cosine Similarity. This leads to same recommendations for different users because popular objects or users often win a heavier weight in the process of recommendation. Moreover, it has been increasingly recognized that the gains of the recommendation accuracy are often accompanied by the losses of the diversity. In order to walk out of the

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... sity dilemma, we propose a new method named collaborative filtering based on random walk with choice which replaces the traditional Pearson Correlation or (adjusted) Cosine Similarity with random walk with choice for calculating users' similarities. Results show that our approach significantly outperforms the ordinary user-based collaborative filtering method in diversity without lowing recommendation accuracy.