TY - GEN
T1 - TAP
T2 - AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events held in conjunction with 18th International Conference on Service-Oriented Computing, ICSOC 2020
AU - Yakhchi, Shahpar
AU - Ghafari, Seyed Mohssen
AU - Orgun, Mehmet
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Recommendation system
KW - Personality information
KW - Trust relation
UR - http://www.scopus.com/inward/record.url?scp=85111392947&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-76352-7_30
DO - 10.1007/978-3-030-76352-7_30
M3 - Conference proceeding contribution
AN - SCOPUS:85111392947
SN - 9783030763510
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 294
EP - 308
BT - Service-Oriented Computing – ICSOC 2020 Workshops
A2 - Hacid, Hakim
A2 - Outay, Fatma
A2 - Paik, Hye-young
A2 - Alloum, Amira
A2 - Petrocchi, Marinella
A2 - Bouadjenek, Mohamed Reda
A2 - Beheshti, Amin
A2 - Liu, Xumin
A2 - Maaradji, Abderrahmane
PB - Springer, Springer Nature
CY - Cham, Switzerland
Y2 - 14 December 2020 through 17 December 2020
ER -