Resolving Data Sparsity and Cold Start in Recommender Systems

Recommender systems (RSs) are heavily used in e-commerce to provide users with high quality, personalized recommendations from a large number of choices. Collaborative filtering (CF) is a widely used technique to generate recommendations [1]. The main research problems we desire to address are the two severe issues that original CF inherently suffers from:

Data sparsity

arises from the phenomenon that users in general rate only a limited number of items;

Cold start

refers to the difficulty in bootstrapping the RSs for new users or new items.

The principle of CF is to aggregate the ratings of like-minded users. However, the reported matrix of user-item ratings is usually very sparse (up to 99%) due to users’ lack of knowledge or incentives to rate items. In addition, for the new users or new items, in general, they report or receive only a few or no ratings. Both issues will prevent the CF from providing effective recommendations, because users’ preference is hard to extract. Although many algorithms have been proposed to date, these issues have not been well-addressed yet.