TEFRCF:标签熵特征表示的协同过滤个性化推荐算法

摘要/Abstract

摘要： 标签作为Web 2.0时代信息分类和检索的有效方式,已经成为近年的热点研究对象。标签推荐系统旨在利用标签数据为用户提供个性化推荐。现有的基于标签的推荐方法在预测用户对物品的兴趣度时往往倾向于赋予热门标签及其对应的热门物品较大的权重,导致权重偏差,降低了推荐结果的新颖性,未能充分反映用户个性化的兴趣。针对上述问题,定义了标签熵的概念来度量标签的不确定性,提出了标签熵特征表示的协同过滤个性化推荐算法。该算法通过引入标签熵来解决权重偏差问题,利用三分图形式描述用户-标签-项目之间的关系;构建基于标签熵特征表示的用户和项目特征表示,并通过特征相似性度量方法计算项目的相似性;最后利用用户标签行为和项目的相似性线性组合预测用户对项目的偏好值,并根据预测偏好值排序生成最终的推荐列表。在Last.fm数据集上的实验结果表明,该方法能够提高推荐准确性和新颖性,满足用户的个性化需求。

关键词: 标签, 熵, 推荐系统, 协同过滤

Abstract: Tags are served as an effective way for information classification and information retrieval at the age of Web2.0.Tag recommendation systems aim to provide personalized recommendation for users by using tag data.Theexi-sting tag-based recommendation methods tend to assign the popular tags and their corresponding items more larger weight in predicting users’ interest on the items,resulting in weight deviations,reducing the novelty of the results and being unable to fully reflect users’ personalized interest.In order to solve the problems above,the concept of tag entropy was defined to measure the uncertainty of tags,and the collaborative filtering personalized recommendation algorithm based on tags entropy feature representation was proposed.This method solves the problem of weight deviation by introducing tag entropy,and then the tripartite graphs are used to describe the relationship among users,tags and items.The representation of users and items is constructed based on tag entropy feature representation,and the similarity of items is calculated by the feature similarity measure method.Finally,the user preferences for items are predicted by the linear combination of tags behaviors and similarity of items,and then the recommended list is generated according to the rank of preferences.The experimental results on Last.fm show that the proposed algorithm can improve recommendation accuracy and novelty,and satisfy the requirement for users.

Key words: Collaborative filtering, Entropy, Recommendation systems, Tag

中图分类号:

TP391

何明, 杨芃, 要凯升, 张久伶. TEFRCF:标签熵特征表示的协同过滤个性化推荐算法[J]. 计算机科学, 2018, 45(6A): 465-470. https://doi.org/

HE Ming, YANG Peng, YAO Kai-sheng, ZHANG Jiu-ling. TEFRCF:Collaborative Filtering Personalized Recommendation Algorithm Based on Tag
Entropy Feature Representation[J]. Computer Science, 2018, 45(6A): 465-470. https://doi.org/

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