Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites

• URL: http://arxiv.org/abs/2209.07584v1
• Date: Thu, 15 Sep 2022 19:46:01 GMT
• Title: Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites
• Authors: Simiao Zuo, Qingyu Yin, Haoming Jiang, Shaohui Xi, Bing Yin, Chao Zhang, Tuo Zhao
• Abstract summary: E-commerce queries are often short and ambiguous. Users tend to enter multiple searches, which we call context, before purchasing. We propose an end-to-end context-aware query rewriting model.
• Score: 47.04727122209316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: E-commerce queries are often short and ambiguous. Consequently, query understanding often uses query rewriting to disambiguate user-input queries. While using e-commerce search tools, users tend to enter multiple searches, which we call context, before purchasing. These history searches contain contextual insights about users' true shopping intents. Therefore, modeling such contextual information is critical to a better query rewriting model. However, existing query rewriting models ignore users' history behaviors and consider only the instant search query, which is often a short string offering limited information about the true shopping intent. We propose an end-to-end context-aware query rewriting model to bridge this gap, which takes the search context into account. Specifically, our model builds a session graph using the history search queries and their contained words. We then employ a graph attention mechanism that models cross-query relations and computes contextual information of the session. The model subsequently calculates session representations by combining the contextual information with the instant search query using an aggregation network. The session representations are then decoded to generate rewritten queries. Empirically, we demonstrate the superiority of our method to state-of-the-art approaches under various metrics. On in-house data from an online shopping platform, by introducing contextual information, our model achieves 11.6% improvement under the MRR (Mean Reciprocal Rank) metric and 20.1% improvement under the HIT@16 metric (a hit rate metric), in comparison with the best baseline method (Transformer-based model).

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