BOOSTING FOR RECOMMENDATION SYSTEMS / BOOSTING PARA SISTEMAS DE RECOMENDAÇÃO

AUTOR(ES)

TULIO JORGE DE A N DE S ANIBOLETE

DATA DE PUBLICAÇÃO

2008

RESUMO

With the amount of information and its easy availability on the Internet, many options are offered to the people and they, normally, have little or almost no experience to decide between the existing alternatives. In this scene, the Recommendation Systems appear to organize and recommend automatically, through Machine Learning, the interesting items. One of the great recommendation challenges is to match correctly what is being recommended and who are receiving the recommendation. This work presents a Recommendation System based on Collaborative Filtering, technique whose essence is the exchange of experiences between users with common interests. In Collaborative Filtering, users rate each experimented item indicating its relevance allowing the use of ratings by other users of the same group. Our objective is to implement a Boosting algorithm in order to optimize a Recommendation System performance. For this, we use a database of advertisements with validation purposes and a database of movies with testing purposes. After adaptations in the conventional Boosting strategies, improvements of 3% were reached over the original algorithm.

ASSUNTO(S)

boosting machine learning recommender systems sistemas de recomendacao filtragem colaborativa collaborative filtering boosting aprendizado de maquina

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