A5015212947- Recommender Systems and Techniques
- Topic Modeling
- Image Retrieval and Classification Techniques
- Data Management and Algorithms
- Caching and Content Delivery
Tokyo University of Science
2017-2022
Recommender systems are beneficial to both service providers and users, as they offer item recommendations individual users based on their preferences. In this paper, we propose a hybrid recommender system widely used topic modeling method: supervised latent Dirichlet allocation (sLDA). This model provides simple clustering method for analyzing large volumes of unlabeled data from potential items. We use matrix factorization sLDA finding in item-feature spaces, enabling using distributions...
Recommender systems are beneficial to both service providers and users, as they offer item recommendations individual users based on their preferences. However, the recommender present cold start problems, which refer fact that it is difficult recommend new items for newly added users. existing have been researched vigorously. On other hand, problems received insufficient attention. In this paper, we propose methods filtering can be applied address problems. Our models provide simple matrix...