Name
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.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3397271.3401089 | SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval
Virtual Event
China
July, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-8016-4 | 5 |
PageRank | References | Authors |
0.42 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuqi Lu | 1 | 9 | 0.80 |
| Zhicheng Dou | 2 | 19 | 5.43 |
| Chen-Yan Xiong | 3 | 405 | 30.82 |
| Xiao-Jie Wang | 4 | 15 | 5.34 |
| Ji-Rong Wen | 5 | 4431 | 265.98 |