Greedy SLIM: A SLIM-Based Approach For Preference Elicitation

TL;DR

Greedy SLIM introduces a novel preference elicitation method based on SLIM, which iteratively selects items to improve recommendation accuracy, outperforming latent factor models in user studies.

Contribution

This paper presents the first SLIM-based approach for preference elicitation, introducing a greedy training technique that enhances cold-start recommendation performance.

Findings

Greedy SLIM outperforms latent factor models in user studies.

The method effectively minimizes SLIM loss through iterative item selection.

Offline experiments confirm the efficiency of the approach.

Abstract

Preference elicitation is an active learning approach to tackle the cold-start problem of recommender systems. Roughly speaking, new users are asked to rate some carefully selected items in order to compute appropriate recommendations for them. To the best of our knowledge, we are the first to propose a method for preference elicitation that is based on SLIM , a state-of-the-art technique for top-N recommendation. Our approach mainly consists of a new training technique for SLIM, which we call Greedy SLIM. This technique iteratively selects items for the training in order to minimize the SLIM loss greedily. We conduct offline experiments as well as a user study to assess the performance of this new method. The results are remarkable, especially with respect to the user study. We conclude that Greedy SLIM seems to be more suitable for preference elicitation than widely used methods based on latent factor models.