基于经验分布和KL散度的协同过滤推荐质量评价研究

Study on recommendation quality evaluation based on empirical distribution and KL divergence

提出了一种基于经验分布和KL散度的协同过滤推荐质量评价方法 RQE-EDKL(recommendation quality evaluation based on empirical distribution and KL divergence)。RQE-EDKL首先利用历史用户-商品数据生成不同商品数量下的商品历史使用概率分布;然后,利用该分布与各个协同过滤推荐方法得到的用户商品使用概率进行比较,计算其KL散度;最后,将KL散度最小的推荐结果视为最佳推荐结果并推送给用户。在Talking Data数据集上的实验结果表明,RQE-EDKL评价方法能够有效地在不同的推荐结果中选择更为切合用户真实需求的推荐结果,从而提高了协同过滤推荐的质量。

This paper proposed an approach called RQE-EDKL (recommendation quality evaluation based on empirical distribution and KL divergence) to evaluate the recommendation quality based on empirical distribution and KL divergence. QE-EDKL firstly made use of historical user-item data to produce the historical usage probability distribution of items at different quantities. Secondly, it calculated the KL divergence based on the distributions of the historical usage probability and the usa-ge probability of different recommendations. Thirdly, it regarded the recommendation with the minimum KL divergence as with the best quality and is recommended to the user. Experiments on TalkingData App data sets demonstrate that RQE-EDKL can effectively improve the quality of recommended results of collaborative filtering significantly on both accuracy and diversity.