research-article

Memory-based Recommendations of Entities for Web Search Users

Authors:

Ignacio Fernández-Tobías,

Roi BlancoAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 35 - 44

https://doi.org/10.1145/2983323.2983823

Published: 24 October 2016 Publication History

Abstract

Modern search engines have evolved from mere document retrieval systems to platforms that assist the users in discovering new information. In this context, entity recommendation systems exploit query log data to proactively provide the users with suggestions of entities (people, movies, places, etc.) from knowledge bases that are relevant for their current information need. Previous works consider the problem of ranking facts and entities related to the user's current query, or focus on specific recommendation domains requiring supervised selection and extraction of features from knowledge bases. In this paper we propose a set of domain-agnostic methods based on nearest neighbors collaborative filtering that exploit query log data to generate entity suggestions, taking into account the user's full search session. Our experimental results on a large dataset from a commercial search engine show that the proposed methods are able to compute relevant entity recommendations outperforming a number of baselines. Finally, we perform an analysis on a cross-domain scenario using different entity types, and conclude that even if knowing the right target domain is important for providing effective recommendations, some inter-domain user interactions are helpful for the task at hand.

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Cited By

Karimi M(2024)SessionPrint: Accelerating kNN via Locality-Sensitive Hashing for Session-Based News RecommendationExperimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-031-71736-9_10(159-165)Online publication date: 14-Sep-2024
https://doi.org/10.1007/978-3-031-71736-9_10
Garigliotti DBalog KHose KBjerva J(2023)Recommending tasks based on search queries and missionsNatural Language Engineering10.1017/S1351324923000219(1-25)Online publication date: 17-May-2023
https://doi.org/10.1017/S1351324923000219
Barman DSarkar RChowdhury N(2023)A cooperative co-evolutionary genetic algorithm for query recommendationMultimedia Tools and Applications10.1007/s11042-023-15585-683:4(11461-11491)Online publication date: 29-Jun-2023
https://doi.org/10.1007/s11042-023-15585-6
Show More Cited By

Index Terms

Memory-based Recommendations of Entities for Web Search Users
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Collaborative filtering

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Published In

cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 ACM.

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Publication History

Published: 24 October 2016

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Spanish Ministry of Science and Innovation

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CIKM'16

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CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

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CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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November 10 - 14, 2025

Seoul , Republic of Korea

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Cited By

Karimi M(2024)SessionPrint: Accelerating kNN via Locality-Sensitive Hashing for Session-Based News RecommendationExperimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-031-71736-9_10(159-165)Online publication date: 14-Sep-2024
https://doi.org/10.1007/978-3-031-71736-9_10
Garigliotti DBalog KHose KBjerva J(2023)Recommending tasks based on search queries and missionsNatural Language Engineering10.1017/S1351324923000219(1-25)Online publication date: 17-May-2023
https://doi.org/10.1017/S1351324923000219
Barman DSarkar RChowdhury N(2023)A cooperative co-evolutionary genetic algorithm for query recommendationMultimedia Tools and Applications10.1007/s11042-023-15585-683:4(11461-11491)Online publication date: 29-Jun-2023
https://doi.org/10.1007/s11042-023-15585-6
Imène SBadia KNadir M(2021)Knowledge Graph-Based Approaches for Related Entities RecommendationArtificial Intelligence and Heuristics for Smart Energy Efficiency in Smart Cities10.1007/978-3-030-92038-8_49(488-496)Online publication date: 25-Nov-2021
https://doi.org/10.1007/978-3-030-92038-8_49
Huang JWang HZhang WLiu T(2020)Multi-Task Learning for Entity Recommendation and Document Ranking in Web SearchACM Transactions on Intelligent Systems and Technology10.1145/339650111:5(1-24)Online publication date: 26-Jul-2020
https://dl.acm.org/doi/10.1145/3396501
Huang JZhang WSun YWang HLiu T(2018)Improving entity recommendation with search log and multi-task learningProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304222.3304341(4107-4114)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304222.3304341
Huang JDing SWang HLiu T(2018)Learning to Recommend Related Entities With Serendipity for Web Search UsersACM Transactions on Asian and Low-Resource Language Information Processing10.1145/318566317:3(1-22)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3185663
Balog KBalog K(2018)Utilizing Entities for an Enhanced Search ExperienceEntity-Oriented Search10.1007/978-3-319-93935-3_9(299-336)Online publication date: 3-Oct-2018
https://doi.org/10.1007/978-3-319-93935-3_9

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