Using contextual information and multidimensional approach for recommendation

It has been recognized that recommendation system is a very important and indispensable topic in E-commerce. Many famous E-commerce websites utilize recommendation systems to convert browsers into buyers. The forms of recommendation include suggesting products/services to the customer, providing personalized product/service information, summarizing community opinion, and providing community critiques. Personalized recommendation methods are mainly classified into content-based recommendation approach and collaborative filtering recommendation approach. Both recommendation approaches, however, have their own drawbacks. This study proposes the integrated contextual information as the foundation concept of multidimensional recommendation model, and uses the online analytical processing (OLAP) ability of data warehousing to solve the contradicting problems among hierarchy ratings. The evaluation studies show that by establishing additional customer profiles and using multidimensional analyses to find the key factors affecting customer perceptions, the proposed approach increases the recommendation quality.