Marivaldo Bispo Rodrigues
Gabriela O. Mota da Silva
Frederico Araújo Durão
ABSTRACT
Recommender systems are used by many sites and services, and are important tools to help the user to find what is most relevant in the immense amount of information available. One way to build a Recommendation System is content-based filtering, which recommends items to the user based on a profile that contains information about the content, such as genre, keywords, etc. For this to happen effectively, the system must take into account the preferences and needs of users in order to generate useful recommendations. This work proposes the modeling of user profiles with integration of multiple domains and automatically. Then, through a transfer of knowledge of a domain to another, increase the performance of the recomendation. The results of the evaluation showed that information sharing between the domains increased the performance of the recommendation, as in the test with the metric prec@5, where obtained an improvement of more than 90\%.
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Index Terms
- User Models Development Based on Cross-Domain for Recommender Systems
Recommendations
Cross-representation mediation of user models
Personalization is considered a powerful methodology for improving the effectiveness of information search and decision making. It has led to the dissemination of systems capable of suggesting relevant and personalized information (or items) to the users,...
A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data MiningRecommender 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 ...
A unified approach to building hybrid recommender systems
RecSys '09: Proceedings of the third ACM conference on Recommender systemsContent-based recommendation systems can provide recommendations for "cold-start" items for which little or no training data is available, but typically have lower accuracy than collaborative filtering systems. Conversely, collaborative filtering ...
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