Preference elicitation techniques for group recommender systems
Information Sciences: an International Journal
Kernel-Mapping Recommender system algorithms
Information Sciences: an International Journal
Semantic preference retrieval for querying knowledge bases
Proceedings of the 1st Joint International Workshop on Entity-Oriented and Semantic Search
A Random Walk Model for Item Recommendation in Social Tagging Systems
ACM Transactions on Management Information Systems (TMIS)
Enhancing the accuracy of ratings predictions of video recommender system by segments of interest
Proceedings of the 19th Brazilian symposium on Multimedia and the web
Contents Recommendation Method Using Social Network Analysis
Wireless Personal Communications: An International Journal
Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems
Expert Systems with Applications: An International Journal
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Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and demographic recommender systems. Collaborative filtering recommender systems recommend items by taking into account the taste (in terms of preferences of items) of users, under the assumption that users will be interested in items that users similar to them have rated highly. Content-based filtering recommender systems recommend items based on the textual information of an item, under the assumption that users will like similar items to the ones they liked before. Demographic recommender systems categorize users or items based on their personal attribute and make recommendation based on demographic categorizations. These systems suffer from scalability, data sparsity, and cold-start problems resulting in poor quality recommendations and reduced coverage. In this paper, we propose a unique cascading hybrid recommendation approach by combining the rating, feature, and demographic information about items. We empirically show that our approach outperforms the state of the art recommender system algorithms, and eliminates recorded problems with recommender systems.