TAP: a two-level trust and personality-aware recommender system

Shahpar Yakhchi*, Seyed Mohssen Ghafari, Mehmet Orgun

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

Recommender systems (RSs) have been adopted in a variety set of web services to provide a list of items which a user may interact with in near future. Collaborative filtering (CF) is one of the most widely used mechanism in RSs that focuses on preferences of neighbours of similar users. Therefore, it is a critical challenge for CF models to discover a set of appropriate neighbors for a particular user. Most of the current approaches exploit users’ ratings information to find similar users by comparing their rating patterns. However, this may be a simple idea and over-tested by the current studies, which may fail under data sparsity problem. Recommender system as an intelligent system needs to help users with their decision making process, and facilitate them with personalized suggestions. In real world, people are willing to share similar interest with those who have the same personality type; and then among all similar personality users pope may only take advice and recommendation from the trustworthy ones. Therefore, in this paper we propose a two-level model, TAP, which analyzes users’ behaviours to first detect their personality types, and then incorporate trust information to provide more customized recommendations. We mathematically model our approach based on the matrix factorization to consider personality and trust information simultaneously. Experimental results on a real-world dataset demonstrate the effectiveness of our model.

Original languageEnglish
Title of host publicationService-Oriented Computing – ICSOC 2020 Workshops
Subtitle of host publicationAIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events Dubai, United Arab Emirates, December 14–17, 2020 Proceedings
EditorsHakim Hacid, Fatma Outay, Hye-young Paik, Amira Alloum, Marinella Petrocchi, Mohamed Reda Bouadjenek, Amin Beheshti, Xumin Liu, Abderrahmane Maaradji
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages294-308
Number of pages15
ISBN (Electronic)9783030763527
ISBN (Print)9783030763510
DOIs
Publication statusPublished - 2021
EventAIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events held in conjunction with 18th International Conference on Service-Oriented Computing, ICSOC 2020 - Virtual, Online
Duration: 14 Dec 202017 Dec 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12632 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceAIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events held in conjunction with 18th International Conference on Service-Oriented Computing, ICSOC 2020
CityVirtual, Online
Period14/12/2017/12/20

Keywords

  • Recommendation system
  • Personality information
  • Trust relation

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