4 research outputs found
Adaptive Matrix Completion for the Users and the Items in Tail
Recommender systems are widely used to recommend the most appealing items to
users. These recommendations can be generated by applying collaborative
filtering methods. The low-rank matrix completion method is the
state-of-the-art collaborative filtering method. In this work, we show that the
skewed distribution of ratings in the user-item rating matrix of real-world
datasets affects the accuracy of matrix-completion-based approaches. Also, we
show that the number of ratings that an item or a user has positively
correlates with the ability of low-rank matrix-completion-based approaches to
predict the ratings for the item or the user accurately. Furthermore, we use
these insights to develop four matrix completion-based approaches, i.e.,
Frequency Adaptive Rating Prediction (FARP), Truncated Matrix Factorization
(TMF), Truncated Matrix Factorization with Dropout (TMF + Dropout) and Inverse
Frequency Weighted Matrix Factorization (IFWMF), that outperforms traditional
matrix-completion-based approaches for the users and the items with few ratings
in the user-item rating matrix.Comment: 7 pages, 3 figures, ACM WWW'1