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).

Related papers

• Improving Retrieval in Sponsored Search by Leveraging Query Context Signals [6.152499434499752]
  We propose an approach to enhance query understanding by augmenting queries with rich contextual signals. We use web search titles and snippets to ground queries in real-world information and utilize GPT-4 to generate query rewrites and explanations. Our context-aware approach substantially outperforms context-free models.
  arXiv  Detail & Related papers  (2024-07-19T14:28:53Z)
• Query-oriented Data Augmentation for Session Search [71.84678750612754]
  We propose query-oriented data augmentation to enrich search logs and empower the modeling. We generate supplemental training pairs by altering the most important part of a search context. We develop several strategies to alter the current query, resulting in new training data with varying degrees of difficulty.
  arXiv  Detail & Related papers  (2024-07-04T08:08:33Z)
• Improving Topic Relevance Model by Mix-structured Summarization and LLM-based Data Augmentation [16.170841777591345]
  In most social search scenarios such as Dianping, modeling search relevance always faces two challenges. We first take queryd with the query-based summary and the document summary without query as the input of topic relevance model. Then, we utilize the language understanding and generation abilities of large language model (LLM) to rewrite and generate query from queries and documents in existing training data.
  arXiv  Detail & Related papers  (2024-04-03T10:05:47Z)
• Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model [78.80174696043021]
  We propose a novel model called the Entity-Based Relevance Model (EBRM) The decomposition allows us to use a Cross-encoder QE relevance module for high accuracy. We also show that pretraining the QE module with auto-generated QE data from user logs can further improve the overall performance.
  arXiv  Detail & Related papers  (2023-07-01T15:44:53Z)
• ConvGQR: Generative Query Reformulation for Conversational Search [37.54018632257896]
  ConvGQR is a new framework to reformulate conversational queries based on generative pre-trained language models. We propose a knowledge infusion mechanism to optimize both query reformulation and retrieval.
  arXiv  Detail & Related papers  (2023-05-25T01:45:06Z)
• Graph Enhanced BERT for Query Understanding [55.90334539898102]
  query understanding plays a key role in exploring users' search intents and facilitating users to locate their most desired information. In recent years, pre-trained language models (PLMs) have advanced various natural language processing tasks. We propose a novel graph-enhanced pre-training framework, GE-BERT, which can leverage both query content and the query graph.
  arXiv  Detail & Related papers  (2022-04-03T16:50:30Z)
• Query Rewriting via Cycle-Consistent Translation for E-Commerce Search [13.723266150864037]
  We propose a novel deep neural network based approach to query rewriting. We formulate query rewriting into a cyclic machine translation problem. We introduce a novel cyclic consistent training algorithm in conjunction with state-of-the-art machine translation models.
  arXiv  Detail & Related papers  (2021-03-01T06:47:12Z)
• Session-Aware Query Auto-completion using Extreme Multi-label Ranking [61.753713147852125]
  We take the novel approach of modeling session-aware query auto-completion as an e Multi-Xtreme Ranking (XMR) problem. We adapt a popular XMR algorithm for this purpose by proposing several modifications to the key steps in the algorithm. Our approach meets the stringent latency requirements for auto-complete systems while leveraging session information in making suggestions.
  arXiv  Detail & Related papers  (2020-12-09T17:56:22Z)
• Query Resolution for Conversational Search with Limited Supervision [63.131221660019776]
  We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC.
  arXiv  Detail & Related papers  (2020-05-24T11:37:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.