Greedy SLIM: A SLIM-Based Approach For Preference Elicitation
This addresses the cold-start problem for new users in recommender systems, but it is incremental as it adapts an existing method.
The paper tackles the cold-start problem in recommender systems by proposing Greedy SLIM, a new training technique for SLIM-based preference elicitation, and finds it outperforms latent factor models in offline experiments and a user study.
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.