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Using Implicit Feedback for Neighbors Selection: Alleviating the Sparsity Problem in Collaborative Recommendation Systems

Authors:

Bruno Bandeira Fernandes,

Juarez A.P. Sacenti,

Roberto WillrichAuthors Info & Claims

WebMedia '17: Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web

Pages 341 - 348

https://doi.org/10.1145/3126858.3126896

Published: 17 October 2017 Publication History

Abstract

The most popular Recommender systems (RSs) employ Collaborative Filtering (CF) algorithms where users explicitly rate items. Based on these ratings, a user-item rating matrix is generated and used to select the items to be recommended for a target user. An important step in this process is to determine the neighborhood of a target user, i.e. a set of users who rate items similarly to this user. One of the limitations of CF is precisely the need of rating data provided voluntarily by users. The lack of interest of users to provide this kind of information increases the sparsity problem of the ratings matrix. In this paper, we propose the use of implicit feedback for neighbors selection to alleviate the sparsity problem in CF-based RSs. In this proposal, user profiles are built based on the characteristics of items that have been accessed or purchased, and not necessarily rated by the users. This user profile is used exclusively to the neighborhoods formation, which considers not how they have rated items, but by the characteristics of the items that they have accessed or purchased. Our technique was implemented with Apache Mahout Framework and evaluated across experiments in the domain of movies by using a dataset from Movielens project. The results demonstrated that our technique produces better quality recommendations when compared to the classic CF mainly in presence of sparsity of rating data.

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Cited By

Lavanya KDorendra BChopdar SMahantshetti A(2024)Real-Time Music Recommendation System Integrating PySpark and Kafka for Enhanced User ExperiencePioneering Approaches in Data Management10.4018/979-8-3693-5563-3.ch008(175-190)Online publication date: 27-Sep-2024
https://doi.org/10.4018/979-8-3693-5563-3.ch008
Kar PRoy MDatta SKar PRoy MDatta S(2024)Collaborative Filtering and Content-Based SystemsRecommender Systems: Algorithms and their Applications10.1007/978-981-97-0538-2_3(19-30)Online publication date: 12-Jun-2024
https://doi.org/10.1007/978-981-97-0538-2_3
Sacenti JFileto RWillrich R(2021)Knowledge graph summarization impacts on movie recommendationsJournal of Intelligent Information Systems10.1007/s10844-021-00650-zOnline publication date: 22-Jun-2021
https://doi.org/10.1007/s10844-021-00650-z
Show More Cited By

Index Terms

Using Implicit Feedback for Neighbors Selection: Alleviating the Sparsity Problem in Collaborative Recommendation Systems
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Collaborative filtering
      2. Social recommendation
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Other conferences

WebMedia '17: Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web

October 2017

522 pages

ISBN:9781450350969

DOI:10.1145/3126858

General Chairs:
Valter Roesler
UFRGS, Brazil
,
José Valdeni de Lima
UFRGS, Brazil
,
Program Chairs:
Celso Alberto Saibel Santos
UFES, Brazil
,
Roberto Willrich
UFSC, Brazil

Copyright © 2017 ACM.

© 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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SBC: Brazilian Computer Society
CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
CGIBR: Comite Gestor da Internet no Brazil
CAPES: Brazilian Higher Education Funding Council

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2017

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Webmedia '17

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SBC
CNPq
CGIBR
CAPES

Webmedia '17: Brazilian Symposium on Multimedia and the Web

October 17 - 20, 2017

RS, Gramado, Brazil

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WebMedia '17 Paper Acceptance Rate 38 of 138 submissions, 28%;

Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

Lavanya KDorendra BChopdar SMahantshetti A(2024)Real-Time Music Recommendation System Integrating PySpark and Kafka for Enhanced User ExperiencePioneering Approaches in Data Management10.4018/979-8-3693-5563-3.ch008(175-190)Online publication date: 27-Sep-2024
https://doi.org/10.4018/979-8-3693-5563-3.ch008
Kar PRoy MDatta SKar PRoy MDatta S(2024)Collaborative Filtering and Content-Based SystemsRecommender Systems: Algorithms and their Applications10.1007/978-981-97-0538-2_3(19-30)Online publication date: 12-Jun-2024
https://doi.org/10.1007/978-981-97-0538-2_3
Sacenti JFileto RWillrich R(2021)Knowledge graph summarization impacts on movie recommendationsJournal of Intelligent Information Systems10.1007/s10844-021-00650-zOnline publication date: 22-Jun-2021
https://doi.org/10.1007/s10844-021-00650-z
Sacenti JWillrich RFileto RNeto MNovais RFerraz CViana W(2018)Hybrid Recommender System Based on Multi-Hierarchical OntologiesProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3243106(149-156)Online publication date: 16-Oct-2018
https://dl.acm.org/doi/10.1145/3243082.3243106
Lessa LBrandão WNeto MNovais RFerraz CViana W(2018)Filtering Graduate Courses based on LinkedIn ProfilesProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3243094(141-147)Online publication date: 16-Oct-2018
https://dl.acm.org/doi/10.1145/3243082.3243094

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