Name
Abstract | ||
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This paper presents Feedback Memory Network (\textttFMN) which models user interactions with the search engine for query suggestion. Besides modeling the queries issued by the user, \textttFMN also considers user feedback on the search results. It converts user browsing and click actions to the attention over the top-ranked documents and combines them into the feedback memories of the query, thus better models the underlying information needs. The feedback memories and the query sequence are then combined to suggest queries by the sequence-to-sequence neural network. Modeling user feedback makes it possible to suggest diverse queries for the same query sequence, if users have preferred different search results that indicate different information needs. Our experiments on the search log from a Chinese commercial search engine showed the stable and robust advantages of \textttFMN. Especially when the feedback is richer or more informative, \textttFMN provides more diverse and accurate suggestions, which is exceptionally helpful for ambiguous sessions where more information is required to infer the search intents.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3178876.3186068 | WWW '18: The Web Conference 2018
Lyon
France
April, 2018 |
Keywords | Field | DocType |
Query Suggestion, Feedback Memory Network, User Modeling | Information needs,Search engine,Information retrieval,Computer science,Artificial intelligence,User modeling,Artificial neural network,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-5639-8 | 8 | 0.45 |
References | Authors | |
27 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Wu | 1 | 88 | 24.43 |
| Chen-Yan Xiong | 2 | 405 | 30.82 |
| Maosong Sun | 3 | 2293 | 162.86 |
| Zhiyuan Liu | 4 | 2037 | 123.68 |