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Click Through Rate Prediction for Local Search Results

Published: 02 February 2017 Publication History

Abstract

With the ubiquity of internet access and location services provided by smartphone devices, the volume of queries issued by users to find products and services that are located near them is rapidly increasing. Local search engines help users in this task by matching queries with a predefined geographical connotation ("local queries") against a database of local business listings.
Local search differs from traditional web-search because to correctly capture users' click behavior, the estimation of relevance between query and candidate results must be integrated with geographical signals, such as distance. The intuition is that users prefer businesses that are physically closer to them. However, this notion of closeness is likely to depend upon other factors, like the category of the business, the quality of the service provided, the density of businesses in the area of interest, etc.
In this paper we perform an extensive analysis of online users' behavior and investigate the problem of estimating the click-through rate on local search (LCTR) by exploiting the combination of standard retrieval methods with a rich collection of geo and business-dependent features. We validate our approach on a large log collected from a real-world local search service. Our evaluation shows that the non-linear combination of business information, geo-local and textual relevance features leads to a significant improvements over state of the art alternative approaches based on a combination of relevance, distance and business reputation.

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Cited By

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  • (2021)RLNF: Reinforcement Learning based Noise Filtering for Click-Through Rate PredictionProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463012(2268-2272)Online publication date: 11-Jul-2021
  • (2019)Estimating node indirect interaction duration to enhance link predictionSocial Network Analysis and Mining10.1007/s13278-019-0561-29:1Online publication date: 25-Apr-2019
  • (2018)A framework for predicting links between indirectly interacting nodesProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3382225.3382340(544-551)Online publication date: 28-Aug-2018
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cover image ACM Conferences
WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining
February 2017
868 pages
ISBN:9781450346757
DOI:10.1145/3018661
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 February 2017

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Author Tags

  1. distance
  2. information retrieval
  3. model

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WSDM '17 Paper Acceptance Rate 80 of 505 submissions, 16%;
Overall Acceptance Rate 498 of 2,863 submissions, 17%

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View all
  • (2021)RLNF: Reinforcement Learning based Noise Filtering for Click-Through Rate PredictionProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463012(2268-2272)Online publication date: 11-Jul-2021
  • (2019)Estimating node indirect interaction duration to enhance link predictionSocial Network Analysis and Mining10.1007/s13278-019-0561-29:1Online publication date: 25-Apr-2019
  • (2018)A framework for predicting links between indirectly interacting nodesProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3382225.3382340(544-551)Online publication date: 28-Aug-2018
  • (2018)Click-through prediction when searching local businessesProceedings of the 5th Spanish Conference on Information Retrieval10.1145/3230599.3230609(1-2)Online publication date: 26-Jun-2018
  • (2018)Q-graphProceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)10.1145/3210259.3210265(1-10)Online publication date: 10-Jun-2018
  • (2018)The Whole-Page Optimization via Dynamic Ad AllocationCompanion Proceedings of the The Web Conference 201810.1145/3184558.3191584(1407-1411)Online publication date: 23-Apr-2018
  • (2018)Characterizing and Predicting Users’ Behavior on Local Search QueriesACM Transactions on the Web10.1145/315705912:2(1-32)Online publication date: 27-May-2018
  • (2018)A Framework for Predicting Links Between Indirectly Interacting Nodes2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)10.1109/ASONAM.2018.8508767(544-551)Online publication date: Aug-2018

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