Improving First-stage Retrieval of Point-of-interest Search by Pre-training Models
-
Published:2023-12-29
Issue:3
Volume:42
Page:1-27
-
ISSN:1046-8188
-
Container-title:ACM Transactions on Information Systems
-
language:en
-
Short-container-title:ACM Trans. Inf. Syst.
Author:
Mei Lang1ORCID,
Mao Jiaxin2ORCID,
Hu Juan2ORCID,
Tan Naiqiang2ORCID,
Chai Hua2ORCID,
Wen Ji-Rong1ORCID
Affiliation:
1. Beijing Key Laboratory of Big Data Management and Analysis Methods, Gaoling School of Artificial Intelligence, Renmin University of China, China
2. Didi Chuxing, China
Abstract
Point-of-interest (POI) search is important for location-based services, such as navigation and online ride-hailing service. The goal of POI search is to find the most relevant destinations from a large-scale POI database given a text query. To improve the effectiveness and efficiency of POI search, most existing approaches are based on a multi-stage pipeline that consists of an efficiency-oriented retrieval stage and one or more effectiveness-oriented re-rank stages. In this article, we focus on the first efficiency-oriented retrieval stage of the POI search. We first identify the limitations of existing first-stage POI retrieval models in capturing the semantic-geography relationship and modeling the fine-grained geographical context information. Then, we propose a Geo-Enhanced Dense Retrieval framework for POI search to alleviate the above problems. Specifically, the proposed framework leverages the capacity of pre-trained language models (e.g., BERT) and designs a pre-training approach to better model the semantic match between the query prefix and POIs. With the POI collection, we first perform a token-level pre-training task based on a geographical-sensitive masked language prediction and design two retrieval-oriented pre-training tasks that link the address of each POI to its name and geo-location. With the user behavior logs collected from an online POI search system, we design two additional pre-training tasks based on users’ query reformulation behavior and the transitions between POIs. We also utilize a late-interaction network structure to model the fine-grained interactions between the text and geographical context information within an acceptable query latency. Extensive experiments on the real-world datasets collected from the Didichuxing application demonstrate that the proposed framework can achieve superior retrieval performance over existing first-stage POI retrieval methods.
Funder
Natural Science Foundation of China
Beijing Outstanding Young Scientist Program
Intelligent Social Governance Platform, Major Innovation Planning Interdisciplinary Platform for the “Double-First Class” Initiative, Renmin University of China
Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,General Business, Management and Accounting,Information Systems
Reference48 articles.
1. Axiomatically Regularized Pre-training for Ad hoc Search
2. Yahui Chen. 2015. Convolutional Neural Network for Sentence Classification. Master’s thesis. University of Waterloo.
3. Deeper Text Understanding for IR with Contextual Neural Language Modeling
4. Improving local identifiability in probabilistic box embeddings;Dasgupta Shib Sankar;arXiv preprint arXiv:2010.04831,2020
5. BERT: Pre-training of deep bidirectional transformers for language understanding;Devlin Jacob;arXiv preprint arXiv:1810.04805,2018
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献