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Title: Context Attentive Document Ranking and Query Suggestion
We present a context-aware neural ranking model to exploit users' on-task search activities and enhance retrieval performance. In particular, a two-level hierarchical recurrent neural network is introduced to learn search context representation of individual queries, search tasks, and corresponding dependency structure by jointly optimizing two companion retrieval tasks: document ranking and query suggestion. To identify variable dependency structure between search context and users' ongoing search activities, attention at both levels of recurrent states are introduced. Extensive experiment comparisons against a rich set of baseline methods and an in-depth ablation analysis confirm the value of our proposed approach for modeling search context buried in search tasks.  more » « less
Award ID(s):
1760523
PAR ID:
10144867
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
Page Range / eLocation ID:
385 to 394
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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