A two-step Recommendation Algorithm via Iterative Local Least Squares

TL;DR

This paper introduces a two-step recommendation algorithm that uses iterative local least squares to improve rating predictions, especially in dense datasets, addressing data sparsity and cold-start issues in recommender systems.

Contribution

The paper proposes a novel two-step algorithm combining ProbS and iterative local least squares to enhance rating estimation and recommendation accuracy.

Findings

Improves AUC accuracy on MovieLens, Netflix, RYM datasets.

Performs better in dense datasets.

Helps in cold-start problem by better estimating missing values.

Abstract

Recommender systems can change our life a lot and help us select suitable and favorite items much more conveniently and easily. As a consequence, various kinds of algorithms have been proposed in last few years to improve the performance. However, all of them face one critical problem: data sparsity. In this paper, we proposed a two-step recommendation algorithm via iterative local least squares (ILLS). Firstly, we obtain the ratings matrix which is constructed via users' behavioral records, and it is normally very sparse. Secondly, we preprocess the "ratings" matrix through ProbS which can convert the sparse data to a dense one. Then we use ILLS to estimate those missing values. Finally, the recommendation list is generated. Experimental results on the three datasets: MovieLens, Netflix, RYM, suggest that the proposed method can enhance the algorithmic accuracy of AUC. Especially, it performs much better in dense datasets. Furthermore, since this methods can improve those missing value more accurately via iteration which might show light in discovering those inactive users' purchasing intention and eventually solving cold-start problem.