research-article

Analyzing the Impact of Social Connections on Rating Behavior in Social Recommender Systems

Author:

Carine Pierrette MukamakuzaAuthors Info & Claims

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

Pages 322 - 326

https://doi.org/10.1145/3079628.3079706

Published: 09 July 2017 Publication History

Get Access

Abstract

Social recommenders provide recommendations taking also into account the social connections among their users. We attempt to formalize and investigate the degree of impact that social connections have to the rating behavior of users, by studying publicly available datasets. Our research will provide a better understanding of specific aspects of social connections that are important when making recommendations, and thus contribute towards more effective social recommenders.

References

[1]

Ido Guy. 2015. Social Recommender Systems. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). Springer, 511--543.

Google Scholar

[2]

Rong Jin, Joyce Y. Chai, and Luo Si. 2004. An automatic weighting scheme for collaborative filtering. In SIGIR. 337--344.

Digital Library

Google Scholar

[3]

Hui Li, Dingming Wu, Wenbin Tang, and Nikos Mamoulis. 2015. Overlapping Community Regularization for Rating Prediction in Social Recommender Systems. In RecSys. 27--34.

Digital Library

Google Scholar

[4]

Hao Ma, Irwin King, and Michael R. Lyu. 2009. Learning to recommend with social trust ensemble. In SIGIR. 203--210.

Digital Library

Google Scholar

[5]

Hao Ma, Haixuan Yang, Michael R. Lyu, and Irwin King. 2008. SoRec: social recommendation using probabilistic matrix factorization. In CIKM. 931--940.

Digital Library

Google Scholar

[6]

Hao Ma, Dengyong Zhou, Chao Liu, Michael R. Lyu, and Irwin King. 2011. Recommender systems with social regularization. In WSDM. 287--296.

Digital Library

Google Scholar

[7]

Paolo Massa and Paolo Avesani. 2007. Trust-aware recommender systems. In RecSys. 17--24.

Digital Library

Google Scholar

[8]

Jason D. M. Rennie and Nathan Srebro. 2005. Fast maximum margin matrix factorization for collaborative prediction. In ICML . 713--719.

Digital Library

Google Scholar

[9]

Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic Matrix Factorization. In NIPS. 1257--1264. http://papers.nips.cc/paper/ 3208-probabilistic-matrix-factorization

Digital Library

Google Scholar

Cited By

View all

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
Mukamakuza CSacharidis DWerthner H(2018)Mining User Behavior in Social Recommender SystemsProceedings of the 8th International Conference on Web Intelligence, Mining and Semantics10.1145/3227609.3227651(1-6)Online publication date: 25-Jun-2018
https://dl.acm.org/doi/10.1145/3227609.3227651

Index Terms

Analyzing the Impact of Social Connections on Rating Behavior in Social Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

Recommendations

Overlapping Community Regularization for Rating Prediction in Social Recommender Systems
RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

Recommender systems have become de facto tools for suggesting items that are of potential interest to users. Predicting a user's rating on an item is the fundamental recommendation task. Traditional methods that generate predictions by analyzing the ...
Toward Social-Semantic Recommender Systems

In this article, the authors consider the basic problem of recommender systems that is identifying a set of users to whom a given item is to be recommended. In practice recommender systems are run against huge sets of users, and the problem is then to ...
A survey of collaborative filtering based social recommender systems

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest to a user items that might be of interest to her. Recent studies demonstrate that information from social networks can be exploited to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

July 2017

420 pages

ISBN:9781450346351

DOI:10.1145/3079628

General Chairs:
Maria Bielikova
Slovak University of Technology in Bratislava, Slovakia
,
Eelco Herder
L3S Research Center, Germany and Radboud Universiteit Nijmegen, The Netherlands
,
Program Chairs:
Federica Cena
University of Torino, Italy
,
Michel Desmarais
Polytechnique University, Montreal, Canada

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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 July 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

UMAP '17

Sponsor:

UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization

July 9 - 12, 2017

Bratislava, Slovakia

Acceptance Rates

UMAP '17 Paper Acceptance Rate 29 of 80 submissions, 36%;

Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Sponsor:
sigchi
sigchi

33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
286
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 16 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

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
Mukamakuza CSacharidis DWerthner H(2018)Mining User Behavior in Social Recommender SystemsProceedings of the 8th International Conference on Web Intelligence, Mining and Semantics10.1145/3227609.3227651(1-6)Online publication date: 25-Jun-2018
https://dl.acm.org/doi/10.1145/3227609.3227651

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Overlapping Community Regularization for Rating Prediction in Social Recommender Systems

Toward Social-Semantic Recommender Systems

A survey of collaborative filtering based social recommender systems