Estudo comparativo de técnicas de seleção de contextos em sistemas de recomendação de domínio cruzado sensivéis ao contexto

Abstract

There are several approaches to implement a recommendation system, such as CrossDomain Context-Aware Recommendation Systems (CD-CARS), which was used in this work because it enables quality improvement of recommendations using multiple domains (e.g. books, movies and musics), while taking into account the use of contexts (e.g. season, time, company and location). However, caution is needed in using contexts to make items suggestions, since the contexts may impair the recommendation performance when they are considered “irrelevants”. Therefore, the selection of relevant contexts is a key factor for the development of CD-CARS, and there is a lack of papers for selection techniques in datasets with contextual information and cross-domain. Thus, this work applied the Information Gain (IG), Chi-square test, Minimum Redundancy Maximum Relevance (MRMR) and Monte Carlo Feature Selection (MCFS) techniques in twelve datasets with three different contextual dimensions (time, location and company) and distinct domains (books, television and musics). Finally, from the results obtained, the MCFS technique was able to classify the relevance of the contexts in a more satisfactory way than other techniques.