Cold-start recommendation through granular association rules
This addresses a rarely considered scenario in e-commerce recommendation, but it appears incremental as it extends existing methods to a specific case.
The paper tackles the cold-start problem in recommender systems when both the user and item are new, proposing an approach based on granular association rules. Results on the MovieLens dataset show that rule sets perform similarly on training and testing sets, with granule setting being crucial.
Recommender systems are popular in e-commerce as they suggest items of interest to users. Researchers have addressed the cold-start problem where either the user or the item is new. However, the situation with both new user and new item has seldom been considered. In this paper, we propose a cold-start recommendation approach to this situation based on granular association rules. Specifically, we provide a means for describing users and items through information granules, a means for generating association rules between users and items, and a means for recommending items to users using these rules. Experiments are undertaken on a publicly available dataset MovieLens. Results indicate that rule sets perform similarly on the training and the testing sets, and the appropriate setting of granule is essential to the application of granular association rules.