research-article

Who should you follow? Combining learning to rank with social influence for informative friend recommendation

Authors:

Chien Chin Chen,

Shun-Yuan Shih,

Meng LeeAuthors Info & Claims

Decision Support Systems, Volume 90, Issue C

Pages 33 - 45

https://doi.org/10.1016/j.dss.2016.06.017

Published: 01 October 2016 Publication History

Abstract

Social network sites have gradually taken the place of traditional media for people to receive the latest information. To receive novel information, users of social network sites are encouraged to establish social relations. The updates shared by friends form social update streams that provide people with up-to-date information. However, having too many friends can lead to an information overload problem causing users to be overwhelmed by the huge number of updates shared continuously by numerous friends. This information overload problem may affect user intentions to join social network sites and thereby possibly reduce the sites' advertising earnings, which are based on the number of users. In this paper, we propose a learning-based recommendation method which suggests informative friends to users, where an informative friend is a friend whose posted updates are liked by the user. Techniques of learning to rank are designed to analyze user behavior and to model the latent preferences of users and updates. At the same time, the learning model is incorporated with social influence to enhance the learned preferences. Informative friends are recommended if the preferences of the updates that they share are highly associated with the preferences of a target user. An effective informative friend recommendation method is developed.The method is capable of overcoming the data sparsity of preference learning.Social influence is helpful for informative friend recommendation.

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cover image Decision Support Systems

Decision Support Systems Volume 90, Issue C

October 2016

112 pages

ISSN:0167-9236

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Copyright © Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 October 2016

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Cited By

Tadson BBoasen JCourtemanche FBeauchemin NKarran ALéger PSénécal S(2023)Neuro-Adaptive Interface System to Evaluate Product Recommendations in the Context of E-CommerceDesign Science Research for a New Society: Society 5.010.1007/978-3-031-32808-4_4(50-68)Online publication date: 31-May-2023
https://dl.acm.org/doi/10.1007/978-3-031-32808-4_4
Zangerle EBauer C(2022)Evaluating Recommender Systems: Survey and FrameworkACM Computing Surveys10.1145/355653655:8(1-38)Online publication date: 23-Dec-2022
https://dl.acm.org/doi/10.1145/3556536
Wang WGuo Q(2021)Subscription strategy choices of network video platforms in the presence of social influenceElectronic Commerce Research10.1007/s10660-021-09504-w23:1(577-604)Online publication date: 30-Aug-2021
https://dl.acm.org/doi/10.1007/s10660-021-09504-w
Starke AWillemsen MSnijders CPaternò FOliver NConati CSpano LTintarev N(2020)With a little help from my peersProceedings of the 25th International Conference on Intelligent User Interfaces10.1145/3377325.3377518(568-578)Online publication date: 17-Mar-2020
https://dl.acm.org/doi/10.1145/3377325.3377518
Yukselen MMutlu AKaragoz PAkerkar RJung J(2019)Infuencee Oriented Topic PredictionProceedings of the 9th International Conference on Web Intelligence, Mining and Semantics10.1145/3326467.3326488(1-9)Online publication date: 26-Jun-2019
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Mario Lustosa Pascoal LAlexandre Dantas do Nascimento HCamilo Junior CQueiroz Da Silva ELima Aleixo ECouto Rosa T(2018)Using Social Information to Compose a Similarity Function Based on Friends Attendance at Events2018 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2018.8477864(1-8)Online publication date: 8-Jul-2018
https://dl.acm.org/doi/10.1109/CEC.2018.8477864
Quijano-Sanchez LLiberatore F(2017)The BIG CHASEDecision Support Systems10.1016/j.dss.2017.04.00798:C(49-58)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.dss.2017.04.007

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