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Investigating algorithmic variations of an RS Graph-based collaborative filtering approach

Published: 07 March 2019 Publication History

Abstract

A personalized recommendation system learns users specific profiles from users feedback and content in order to deliver information tailored to each individual user's interest. Although great effort has been devoted to the proposal, implementation and study of recommendation systems approaches, there is still a lot of room for improvement. Currently, much research on recommender systems focuses on improving the accuracy of their algorithms. In a recent work, we proposed a novel Graph-based Collaborative Filtering approach for recommendation systems based on five steps: (1)-Creating a Homophily network using similarity measures, (2)- Identifying communities in the Homophily network, (3)- Identifying key nodes per community, (4)- Profiling key nodes per community and (5)-Computing recommendations for community users based on the resulting profiles. This paper delves deeper into the first two steps of our approach. In fact, the wide range of algorithms for community detection compelled us to create variations of our approach and to conduct a comparative analysis of their accuracy and time of execution. Results from testing our variations on two open datasets present comparable results. While the Louvain algorithm has the merit of simplicity and straightforwardness, AHC and K-means algorithms render better results when dealing with a larger datasets.

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  • (2024)DFI-DGCF: A Graph-Based Recommendation Approach for Drug-Food InteractionsComplex Networks & Their Applications XII10.1007/978-3-031-53468-3_33(389-399)Online publication date: 20-Feb-2024

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cover image ACM Other conferences
ArabWIC 2019: Proceedings of the ArabWIC 6th Annual International Conference Research Track
March 2019
136 pages
ISBN:9781450360890
DOI:10.1145/3333165
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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  • Google Inc.
  • Microsoft: Microsoft
  • Facebook: Facebook
  • ORACLE: ORACLE
  • IBM: IBM

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 March 2019

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Author Tags

  1. Collaborative Filtering
  2. Community Detection
  3. Homophily
  4. Key node identification
  5. Recommendation systems
  6. Social Network Analysis (SNA)

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ArabWIC 2019

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ArabWIC 2019 Paper Acceptance Rate 20 of 36 submissions, 56%;
Overall Acceptance Rate 20 of 36 submissions, 56%

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View all
  • (2024)DFI-DGCF: A Graph-Based Recommendation Approach for Drug-Food InteractionsComplex Networks & Their Applications XII10.1007/978-3-031-53468-3_33(389-399)Online publication date: 20-Feb-2024

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