Promoting the performance of vertical recommendation systems by applying new classification techniques

Recommender systems (RSs) have proven to be valuable means for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. RSs are software tools providing suggestions for items of interest to users; hence, they typically apply techniques and methodologies from Data Mining. The most frequently used technique is the classification as it matches the aims of RSs that basically classify items based on user’s preferences. The main contribution of this paper is in the area of applying classification techniques to enhance the performance of RSs. In this paper, an Intelligent Adaptive Vertical Recommendation (IAVR) system will be introduced. IAVR recommends text documents related to a specific domain. Basically, the paper concentrates on the first phase of IAVR, which contains two modules; the first is a distiller, while the second is a multi-class classifier. The proposed distiller is employed as a binary classifier that elects documents related to the domain of interest. It is built upon a novel neuro-fuzzy system as well as a modified K Nearest Neighbors (KNN) classifier. On the other hand, the proposed multi-class classifier merges a new instance of Naïve Bayes (NB) classifier, that depends on a proposed learning technique called “accumulative learning”, with association rules. Experimental results have proven the effectiveness of the proposed classifiers, which accordingly promote the overall system’s recommendation accuracy.