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Learning to Recommend Related Entities to Search Users

Published: 02 February 2015 Publication History

Abstract

Over the past few years, major web search engines have introduced knowledge bases to offer popular facts about people, places, and things on the entity pane next to regular search results. In addition to information about the entity searched by the user, the entity pane often provides a ranked list of related entities. To keep users engaged, it is important to develop a recommendation model that tailors the related entities to individual user interests. We propose a probabilistic Three-way Entity Model (TEM) that provides personalized recommendation of related entities using three data sources: knowledge base, search click log, and entity pane log. Specifically, TEM is capable of extracting hidden structures and capturing underlying correlations among users, main entities, and related entities. Moreover, the TEM model can also exploit the click signals derived from the entity pane log. We further provide an inference technique to learn the parameters in TEM, and propose a principled preference learning method specifically designed for ranking related entities. Extensive experiments with two real-world datasets show that TEM with our probabilistic framework significantly outperforms a state of the art baseline, confirming the effectiveness of TEM and our probabilistic framework in related entity recommendation.

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    cover image ACM Conferences
    WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining
    February 2015
    482 pages
    ISBN:9781450333177
    DOI:10.1145/2684822
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    Publication History

    Published: 02 February 2015

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    Author Tags

    1. entity pane
    2. recommender systems
    3. related entities
    4. three-way entity model

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    WSDM '15 Paper Acceptance Rate 39 of 238 submissions, 16%;
    Overall Acceptance Rate 498 of 2,863 submissions, 17%

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    • (2024)Event Recommendation Through Language-Specific User Behaviour in ClickstreamsEvent Analytics across Languages and Communities10.1007/978-3-031-64451-1_8(149-168)Online publication date: 17-Jun-2024
    • (2023)Recommending tasks based on search queries and missionsNatural Language Engineering10.1017/S1351324923000219(1-25)Online publication date: 17-May-2023
    • (2023)LaSERWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2022.10075975:COnline publication date: 1-Jan-2023
    • (2022)User Access Models to Event-Centric InformationCompanion Proceedings of the Web Conference 202210.1145/3487553.3524193(329-333)Online publication date: 25-Apr-2022
    • (2021)Diversity-Aware Entity Exploration on Knowledge GraphIEEE Access10.1109/ACCESS.2021.31077329(118782-118793)Online publication date: 2021
    • (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
    • (2021)Related Entity Expansion and Ranking Using Knowledge GraphComplex, Intelligent and Software Intensive Systems10.1007/978-3-030-79725-6_17(172-184)Online publication date: 30-Jun-2021
    • (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
    • (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
    • (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
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