Abstract
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.
Highlights
Since the late twentieth century, the number of Internet users has increased rapidly; many of them interact through online social networks, effectively making the web community similar to actual society
PROPOSED APPROACH we address how to alleviate the item-side cold-start problems for new items within recommendation systems using the concept of representative reviewers within the rater groups
The example consists of three steps: (i) calculating the average rating based on those provided by the user-item matrix for Item I1; (ii) generating the average preference for the test item; and (iii) calculating the mean absolute error (MAE) between the average rating drawn by the user-item matrix and the average preferences generated by the proposed approach
Summary
Since the late twentieth century, the number of Internet users has increased rapidly; many of them interact through online social networks, effectively making the web community similar to actual society. 2) RECOMMENDER SYSTEMS BASED ON DEEP NEURAL NETWORKS In many studies, supervised learning-based machine learning approaches have been used to address the cold-start problem in recommender systems [28]–[30]. Unlike legacy works, this study addresses the item-side cold-start problems using the concept of weak supervision, which can be used in a content-based recommendation system such as news recommendations. This prediction can provide useful and previously inaccessible information on new items and minimize problems related to the exclusion of new items from the recommendation process. We proposed a weak supervision-based novel approach that exploits content-based filtering and the activities of representative users to predict the preferences for new items in cold-start situations.
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