Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems

Nowadays, there is a constant need for personalization in e-commerce systems. Recommender systems make suggestions and provide information about items available, however, many recommender techniques are still vulnerable to some shortcomings. In this work, we analyze how methods employed in these systems are affected by some typical drawbacks. Hence, we conduct a case study using data gathered from real recommender systems in order to investigate what machine learning methods can alleviate such drawbacks. Due to some especial features inherited by associative classifiers, we give a particular attention to this category of methods to test their capability of dealing with typical drawbacks.