Improved Listwise Collaborative Filtering with High-Rating-Based Similarity and Temporary Ratings

In this paper, we make two proposals to speed up listwise collaborative filtering and improve its accuracy. The first is to speed up computation by only using a subset of the rating information (the high ratings). The second is to improve accuracy using temporary ratings that estimate the rating scores that neighboring users are not rating. Experiments using MovieLens datasets (1M and 10M) demonstrate that these proposals effectively reduce computation time about 1/50 and improve accuracy 2.22% compared with ListCF, a well-known listwise collaborative filtering algorithm. Author