Paper Info
Title
An Active and Deep Semantic Matching Framework for Query Rewrite in E-Commercial Search Engine
Abstract
In order to make the query retrieve much more related products, some query rewrite methods have been proposed to obtain a set of candidate queries which can infer users' search intents and reduce the vocabulary gap between the original query and title of related products. However, previous studies ignore that some candidate queries may change users' search intents and retrieve irrelevant products. As a result, users' search experience will be impacted significantly. To reduce this influence, we need to design a semantic matching model to determine whether the candidate query change the original query's search intents (semantics). In addition, building a semantic matching model faces the following challenges: 1) Queries are usually very short and have limited information. It is very hard to learn an effective semantic matching model with the textual information of queries and candidate queries. 2) In order to get a generalized and effective mode, sufficient data samples are required to train the model. However, the cost of labeling is very huge. In order to address the above challenges, we propose an active and deep semantic matching framework (ActiveMatch) which is composed of two components. One component is the deep semantic matching (DSM) model which can make full use of the search log information to enhance the representation of queries and candidate queries. Then, it can estimate the semantic similarity between the original query and the candidate query more accurately. The other component is an uncertainty and novelty sampling (UNS) strategy which selects the samples to label based on the difficulty of the model estimating and the probability of the occurrence of new words. It not only reduces the cost of labeling but also ensures the effectiveness of the model. The experimental results on the Taobao e-commercial search platform verify the effectiveness of our framework.
Year
DOI
Venue
2019
10.1145/3357384.3358012
Proceedings of the 28th ACM International Conference on Information and Knowledge Management
Keywords
Field
DocType
active learning, deep learning, query rewrite, query understanding
Search engine,Information retrieval,Computer science,Semantic matching
Conference
ISBN
Citations 
PageRank 
978-1-4503-6976-3
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Yatao Yang123.07
Jun Tan2151.61
Hongbo Deng386141.00
Zibin Zheng43731199.37
Yutong Lu530753.61
Xiangke Liao662274.79