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Taste Transitivity for Collaborative Filtering: A Stochastic Network Dynamics Approach
Author(s) -
Xu Minghong,
Bhattacharyya Siddhartha
Publication year - 2021
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/deci.12347
Subject(s) - transitive relation , collaborative filtering , taste , computer science , popularity , dynamics (music) , similarity (geometry) , recommender system , network dynamics , human–computer interaction , artificial intelligence , information retrieval , psychology , social psychology , mathematics , pedagogy , combinatorics , neuroscience , discrete mathematics , image (mathematics)
ABSTRACT We develop a stochastic actor‐based network model for online movie reviews and analyze social network dynamics to study the user‐movie taste networks constructed from real movie review datasets. Examining such taste networks provides useful insights into factors underlying the performance of collaborative filtering‐based recommenders. Our results show that similar taste transitivity effect does exist that support collaborative filtering methods to recommend movies based on user taste similarity. We also investigate the role of movie popularity, genre, user gender, age, and geographic location in the taste network evolution. The findings provide insights to improve current movie recommendation systems. The two‐ mode network analyses approach taken in this article will also be broadly useful in obtaining better understanding of factors that drive user appreciations for varied products.