Listwise Collaborative Filtering with High-Rating-Based Similarity and Simple Missing Value Estimation

Abstract

In this paper, we make two proposals. The first aims to accelerate similarity calculations by only using a subset of the rating information (namely the highest ratings), while the second attempts to improve the accuracy of listwise collaborative filtering using a simple missing value estimation process. Experiments using the MovieLens 1M (6,040 users, 3,952 items and 1,000,209 ratings), 10M (71,567 users, 10,681 items and 10,000,054 ratings) and Jester (48,483 users, 100 items and 3,519,448 ratings) datasets demonstrate that these proposals can considerably reduce the computation time (by a factor of up to 50) and improve the normalized discounted cumulative gain value by up to 0.02 compared with ListCF, a well-known listwise collaborative filtering algorithm.