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| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Listwise Collaborative Filtering | |||||||||||
| 文献作者 | Shanshan Huang; Shuaiqiang Wang; Tie-Yan Liu | ||||||||||
| 文献发表年限 | 2015 | ||||||||||
| 文献关键字 | |||||||||||
| Recommender systems; Collaborative filtering; Ranking- oriented collaborative filtering; 肯德尔; Kendall; Cross Entroy; 交叉熵 | |||||||||||
| 摘要描述 | |||||||||||
| Recently, ranking-oriented collaborative filtering (CF) algorithms have achieved great success in recommender systems. They obtained state-of-the-art performances by estimating a preference ranking of items for each user rather than estimating the absolute ratings on unrated items (as conventional rating-oriented CF algorithms do). In this paper, we propose a new ranking-oriented CF algorithm, called ListCF. Following the memory-based CF framework, ListCF directly predicts a total order of items for each user based on similar users’ probability distributions over permutations of the items, and thus differs from previous ranking-oriented memory-based CF algorithms that focus on predicting the pairwise preferences between items. One important advantage of ListCF lies in its ability of reducing the computational complexity of the training and prediction procedures while achieving the same or better ranking performances as compared to previous ranking-oriented memory-based CF algorithms. Extensive experiments on three benchmark datasets against several state-of-the-art baselines demonstrate the effectiveness of our proposal. | |||||||||||
