Paper Info
Title
AR-CF: Augmenting Virtual Users and Items in Collaborative Filtering for Addressing Cold-Start Problems
Abstract
Cold-start problems are arguably the biggest challenges faced by collaborative filtering (CF) used in recommender systems. When few ratings are available, CF models typically fail to provide satisfactory recommendations for cold-start users or to display cold-start items on users' top-N recommendation lists. Data imputation has been a popular choice to deal with such problems in the context of CF, filling empty ratings with inferred scores. Different from (and complementary to) data imputation, this paper presents AR-CF, which stands for Augmented Reality CF, a novel framework for addressing the cold-start problems by generating virtual, but plausible neighbors for cold-start users or items and augmenting them to the rating matrix as additional information for CF models. Notably, AR-CF not only directly tackles the cold-start problems, but is also effective in improving overall recommendation qualities. Via extensive experiments on real-world datasets, AR-CF is shown to (1) significantly improve the accuracy of recommendation for cold-start users, (2) provide a meaningful number of the cold-start items to display in top-N lists of users, and (3) achieve the best accuracy as well in the basic top-N recommendations, all of which are compared with recent state-of-the-art methods.
Year
DOI
Venue
2020
10.1145/3397271.3401038
SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval Virtual Event China July, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-8016-4
0
PageRank 
References 
Authors
0.34
8
4
Name
Order
Citations
PageRank
Dong-Kyu Chae15910.07
Jihoo Kim200.34
Duen Horng Chau3126086.87
Sang-Wook Kim4792152.77