Knowledge Enhanced Personalized Search
| Autor: | Xiaojie Wang, Ji-Rong Wen, Zhicheng Dou, Shuqi Lu, Chenyan Xiong |
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| Rok vydání: | 2020 |
| Předmět: |
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Information retrieval Computer science business.industry media_common.quotation_subject 05 social sciences 02 engineering and technology Ranking (information retrieval) Personalized search Entity linking Ranking Knowledge base 020204 information systems 0202 electrical engineering electronic engineering information engineering Quality (business) 0509 other social sciences 050904 information & library sciences Representation (mathematics) business media_common |
| Zdroj: | SIGIR |
| DOI: | 10.1145/3397271.3401089 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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