Knowledge Enhanced Personalized Search

Shuqi Lu, Zhicheng Dou, Chenyan Xiong, Xiaojie Wang, Ji-rong Wen
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引用次数: 15

Abstract

This paper presents a knowledge graph enhanced personalized search model, KEPS. For each user and her queries, KEPS first con- ducts personalized entity linking on the queries and forms better intent representations; then it builds a knowledge enhanced profile for the user, using memory networks to store the predicted search intents and linked entities in her search history. The knowledge enhanced user profile and intent representation are then utilized by KEPS for better, knowledge enhanced, personalized search. Furthermore, after providing personalized search for each query, KEPS leverages user's feedback (click on documents) to post-adjust the entity linking on previous queries. This fixes previous linking errors and improves ranking quality for future queries. Experiments on the public AOL search log demonstrate the advantage of knowledge in personalized search: personalized entity linking better reflects user's search intent, the memory networks better maintain user's subtle preferences, and the post linking adjustment fixes some linking errors with the received feedback signals. The three components together lead to a significantly better ranking accuracy of KEPS.
知识增强个性化搜索
提出了一种知识图谱增强的个性化搜索模型KEPS。对于每个用户及其查询,KEPS首先在查询上进行个性化的实体链接,形成更好的意图表示;然后,它为用户建立一个知识增强的配置文件,使用记忆网络存储预测的搜索意图和搜索历史中的链接实体。知识增强的用户档案和意图表示随后被KEPS用于更好的、知识增强的、个性化的搜索。此外,在为每个查询提供个性化搜索之后,KEPS利用用户的反馈(点击文档)对先前查询上的实体链接进行后期调整。这修复了以前的链接错误,并提高了未来查询的排名质量。在AOL公共搜索日志上的实验证明了知识在个性化搜索中的优势:个性化实体链接能更好地反映用户的搜索意图,记忆网络能更好地维护用户的微妙偏好,后链接调整能根据接收到的反馈信号修正一些链接错误。这三个组成部分共同提高了KEPS的排序精度。
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