Alleviating Item-Side Cold-Start Problems in Recommender Systems Using Weak Supervision

Recently, recommender systems have been used in various fields. However, they are still plagued by many issues, including cold-start and sparsity problems. The cold-start problem occurs when users are unable to make recommendations to other users owing to a complete lack of information about certain items. This problem can exist both at the user side and the item side. User-side cold-start problems occur when new users access the systems; item-side cold-start problems occur when new items are added to databases. In this study, we addressed the item-side cold-start problem using the concept of weak supervision. First, a new process for identifying feature based representative reviewers in a rater group was designed. Then, we developed a method to predict the expected preferences for new items by combining content-based filtering and the preferences of representative users. Through extensive experiments, we first confirmed that in comparison to existing methods, the proposed approach provided enhanced accuracy, which was evaluated by determining a mean absolute error for the average ratings. Then, we compared the proposed scheme with the collaborative filtering (CF) and neural CF approaches (NCF). The estimation by the proposed approach was 21% and 38% more accurate than CF and NCF in terms of mean absolute error (MAE), respectively. In future, the proposed framework can be applied in various recommender systems as a core function.