ESLI: Enhancing slope one recommendation through local information embedding

Abstract

Slope one is a popular recommendation algorithm due to its simplicity and high efficiency for sparse data. However, it often suffers from under-fitting since the global information of all relevant users/items are considered. In this paper, we propose a new scheme called enhanced slope one recommendation through local information embedding. First, we employ clustering algorithms to obtain the user clusters as well as item clusters to represent local information. Second, we predict ratings using the local information of users and items in the same cluster. The local information can detect strong localized associations shared within clusters. Third, we design different fusion approaches based on the local information embedding. In this way, both under-fitting and over-fitting problems are alleviated. Experiment results on the real datasets show that our approaches defeats slope one in terms of both mean absolute error and root mean square error.

Ideal User Group: A New Perspective on Item Representation for Recommender Systems

“…In the era of information overload, recommendation algorithms must extensively explore user preferences and item features to make highly effective recommendations. Rating records and item features are the most commonly used in feature mining [1]. Existing methods for representing items focus on the inherent features of items, especially recommendation algorithms that rely on knowledge graphs to model the high-order connectivities [2][3][4][5].…”