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Mining User Behavior in Social Recommender Systems

Authors:

Carine Mukamakuza,

Dimitris Sacharidis,

Hannes WerthnerAuthors Info & Claims

WIMS '18: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics

Article No.: 37, Pages 1 - 6

https://doi.org/10.1145/3227609.3227651

Published: 25 June 2018 Publication History

Abstract

Social recommender systems make use of the available information about social connections between users to improve the quality of the recommendations. The assumption is that if two users are connected, they are likely to have similar preferences, and thus the system should make similar recommendations. Recently many approaches have been proposed based around similar assumptions, whose validity however has not been systematically studied. In our work we make the first step towards examining whether there exist observable relationships between social connections and rating behavior in social recommenders. In particular, we examine publicly available datasets containing traces of rating behavior along with a social graph. Using techniques from social network analysis and statistics, we investigate whether heavy rates, having provided feedback on many items, are also popular, i.e., central in the social network, and vice versa. Our results indicate important connections between heaviness and popularity. Specifically, we find that heaviness implies popularity, and that the association is stronger among very heavy raters.

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Cited By

Margaris DSpiliotopoulos DVassilakis C(2021)Identifying Reliable Recommenders in Users’ Collaborating Filtering and Social NeighbourhoodsBig Data and Social Media Analytics10.1007/978-3-030-67044-3_3(51-76)Online publication date: 6-Jul-2021
https://doi.org/10.1007/978-3-030-67044-3_3
Mishra RRathi S(2020)Efficient and Scalable Job Recommender System Using Collaborative FilteringICDSMLA 201910.1007/978-981-15-1420-3_91(842-856)Online publication date: 19-May-2020
https://doi.org/10.1007/978-981-15-1420-3_91
Mukamakuza CSacharidis DWerthner HPapadopoulos GSamaras GWeibelzahl SJannach DSantos O(2019)The Impact of Social Connections in PersonalizationAdjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization10.1145/3314183.3323675(337-342)Online publication date: 6-Jun-2019
https://dl.acm.org/doi/10.1145/3314183.3323675

Index Terms

Mining User Behavior in Social Recommender Systems
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. Social recommendation

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cover image ACM Other conferences

WIMS '18: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics

June 2018

398 pages

ISBN:9781450354899

DOI:10.1145/3227609

Copyright © 2018 ACM.

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2018

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WIMS '18

WIMS '18: 8th International Conference on Web Intelligence, Mining and Semantics

June 25 - 27, 2018

Novi Sad, Serbia

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Overall Acceptance Rate 140 of 278 submissions, 50%

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Cited By

Margaris DSpiliotopoulos DVassilakis C(2021)Identifying Reliable Recommenders in Users’ Collaborating Filtering and Social NeighbourhoodsBig Data and Social Media Analytics10.1007/978-3-030-67044-3_3(51-76)Online publication date: 6-Jul-2021
https://doi.org/10.1007/978-3-030-67044-3_3
Mishra RRathi S(2020)Efficient and Scalable Job Recommender System Using Collaborative FilteringICDSMLA 201910.1007/978-981-15-1420-3_91(842-856)Online publication date: 19-May-2020
https://doi.org/10.1007/978-981-15-1420-3_91
Mukamakuza CSacharidis DWerthner HPapadopoulos GSamaras GWeibelzahl SJannach DSantos O(2019)The Impact of Social Connections in PersonalizationAdjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization10.1145/3314183.3323675(337-342)Online publication date: 6-Jun-2019
https://dl.acm.org/doi/10.1145/3314183.3323675

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