Abstract
Recommender systems utilize the times of yore experiences and preferences of the target customers as a basis to proffer personalized recommendations for them as well as resolve the information overloading hitch. Personalized recommendation methods are primarily classified into content-based recommendation approach and collaborative filtering recommendation approach. Both recommendation approaches have their own advantages, drawbacks and complementarities. Because conventional recommendation techniques don’t consider the contextual information, the real factor why a customer likes a specific product is unable to be understood. Therefore, in reality, it often causes a decrease in the accuracy of the recommendation results and also persuades the recommendation quality. In this paper, we propose the integrated contextual information as the foundation concept of multidimensional recommendation model and use the Online Analytical Processing (OLAP) ability of data warehousing to solve the contradicting tribulations among hierarchy ratings. This work hopes that by establishing additional user profiles and multidimensional analysis to find the key factors affecting user perceptions, it would increase the recommendation quality.
Highlights
Recommender systems use the earlier period experiences and preferences of the target customers as a foundation to provide personalized recommendations for them and to solve the information overloading problem
The customer list (CL) produced from stage 1 is used as the subject of comparison in multidimensional collaborative filtering recommendation
With the above said capabilities, the recommender system could simultaneously possess the advantages of content-based recommendation, knowledge-based recommendation, collaborative filtering recommendation and Online Analytical Processing (OLAP) in segmenting the information
Summary
Recommender systems use the earlier period experiences and preferences of the target customers as a foundation to provide personalized recommendations for them and to solve the information overloading problem. The recommender system is limited to E-commerce It is applicable for searching the most appropriate results in various search systems used in libraries these days. Majority of the recommender systems use the gathered data under similar environment to provide recommendations. It was discovered, in an actual experience, that if only customer’s past behaviors were considered and the contextual information were ignored, it often caused suspicion in the recommendation results [8]. This research uses the multidimensional recommendation model [1] as the foundation to establish a recommendation structure with multidimensional data collection and analysis ability and solve the book recommendation problems with the use of hierarchy processing and aggregate calculating capabilities. To use multi-facets in demonstrating how MD recommendation model ratings forecast the results
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