research-article

On building entity recommender systems using user click log and freebase knowledge

Authors:

Bo-June (Paul) Hsu,

Jiawei HanAuthors Info & Claims

WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

Pages 263 - 272

https://doi.org/10.1145/2556195.2556233

Published: 24 February 2014 Publication History

Abstract

Due to their commercial value, search engines and recommender systems have become two popular research topics in both industry and academia over the past decade. Although these two fields have been actively and extensively studied separately, researchers are beginning to realize the importance of the scenarios at their intersection: providing an integrated search and information discovery user experience. In this paper, we study a novel application, i.e., personalized entity recommendation for search engine users, by utilizing user click log and the knowledge extracted from Freebase.

To better bridge the gap between search engines and recommender systems, we first discuss important heuristics and features of the datasets. We then propose a generic, robust, and time-aware personalized recommendation framework to utilize these heuristics and features at different granularity levels. Using movie recommendation as a case study, with user click log dataset collected from a widely used commercial search engine, we demonstrate the effectiveness of our proposed framework over other popular and state-of-the-art recommendation techniques.

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Cited By

Zhou YFang YMa CHou THuang X(2024)Efficient Maximal Motif-Clique Enumeration over Large Heterogeneous Information NetworksProceedings of the VLDB Endowment10.14778/3681954.368197517:11(2946-2959)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3681975
Perevalov AAbdollahi SGottschalk SBoth A(2024)Evaluating Entity Importance in a Cross-National Context using Crowdsourcing and Best–Worst ScalingProcedia Computer Science10.1016/j.procs.2024.09.595246(1479-1487)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.09.595
Guesmi MChatti MKadhim LJoarder SAin Q(2023)Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence EncodersMultimodal Technologies and Interaction10.3390/mti70900917:9(91)Online publication date: 15-Sep-2023
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Index Terms

On building entity recommender systems using user click log and freebase knowledge
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Conferences

WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

February 2014

712 pages

ISBN:9781450323512

DOI:10.1145/2556195

General Chairs:
Ben Carterette
University of Delaware, USA
,
Fernando Diaz
Microsoft Research, USA
,
Program Chairs:
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Donald Metzler
Google, USA

Copyright © 2014 ACM.

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Published: 24 February 2014

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WSDM 2014: Seventh ACM International Conference on Web Search and Data Mining

February 24 - 28, 2014

New York, New York, USA

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WSDM '14 Paper Acceptance Rate 64 of 355 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Zhou YFang YMa CHou THuang X(2024)Efficient Maximal Motif-Clique Enumeration over Large Heterogeneous Information NetworksProceedings of the VLDB Endowment10.14778/3681954.368197517:11(2946-2959)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3681975
Perevalov AAbdollahi SGottschalk SBoth A(2024)Evaluating Entity Importance in a Cross-National Context using Crowdsourcing and Best–Worst ScalingProcedia Computer Science10.1016/j.procs.2024.09.595246(1479-1487)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.09.595
Guesmi MChatti MKadhim LJoarder SAin Q(2023)Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence EncodersMultimodal Technologies and Interaction10.3390/mti70900917:9(91)Online publication date: 15-Sep-2023
https://doi.org/10.3390/mti7090091
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
Ghnemat RShaout A(2022)Hybrid Fuzzy Neural Search Retrieval SystemInternational Journal of Enterprise Information Systems10.4018/IJEIS.201607010512:3(1-16)Online publication date: 16-Aug-2022
https://dl.acm.org/doi/10.4018/IJEIS.2016070105
Chatti MJi FGuesmi MMuslim ASingh RJoarder S(2021)SIMT: A Semantic Interest Modeling ToolkitAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3461676(75-78)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450614.3461676
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
Ni CSum Liu KTorzec N(2020)Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge GraphCompanion Proceedings of the Web Conference 202010.1145/3366424.3383570(811-818)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366424.3383570
Li JXing ZSun A(2019)LinkLiveWorld Wide Web10.1007/s11280-018-0621-y22:4(1699-1725)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s11280-018-0621-y
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