Paper Info
Title
Generic Intent Representation in Web Search
Abstract
This paper presents GEneric iNtent Encoder (GEN Encoder) which learns a distributed representation space for user intent in search. Leveraging large scale user clicks from Bing search logs as weak supervision of user intent, GEN Encoder learns to map queries with shared clicks into similar embeddings end-to-end and then fine-tunes on multiple paraphrase tasks. Experimental results on an intrinsic evaluation task - query intent similarity modeling - demonstrate GEN Encoder's robust and significant advantages over previous representation methods. Ablation studies reveal the crucial role of learning from implicit user feedback in representing user intent and the contributions of multi-task learning in representation generality. We also demonstrate that GEN Encoder alleviates the sparsity of tail search traffic and cuts down half of the unseen queries by using an efficient approximate nearest neighbor search to effectively identify previous queries with the same search intent. Finally, we demonstrate distances between GEN encodings reflect certain information seeking behaviors in search sessions.
Year
DOI
Venue
2019
10.1145/3331184.3331198
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
Keywords
Field
DocType
generic intent representation, query embedding, user intent
Information retrieval,Computer science,Information seeking,Paraphrase,Encoder,User intent,Search intent,Distributed representation,Generality,Nearest neighbor search
Conference
ISSN
ISBN
Citations 
SIGIR 2019: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
978-1-4503-6172-9
4
PageRank 
References 
Authors
0.42
0
7
Name
Order
Citations
PageRank
Hongfei Zhang150.77
Xia Song2303.19
Chen-Yan Xiong340530.82
Corby Rosset471.14
Paul N. Bennett5150087.93
Nick Craswell63942279.60
saurabh tiwary7293.86