Improving the effectiveness of experiential decisions by recommendation systems

• We proposed a movie recommendation system (RS) integrating Self-Organizing Map (SOM) with a Neural Network System (NNS).
• We used the MovieLens 100 k database from the “GroupLens Research Project” of The University of Minnesota to test the RS.
• The database contained over 100 thousand ratings of 943 users on 1,682 movies during October 1997 to April 1998.
• The SOM produced 4 clusters of audience with high similarity and these clusters became the four nodes of the hidden layer in NNS.
• The RS provided richer user experiences and improved the accuracy of predicting movie ratings and the speed of data transfer.

Providing experience-oriented offerings through e-commerce is an issue increasing critical in the growing commoditization of e-commercial services. The high accuracy of predictions rendered by Recommendation System (RS) technologies has strengthened the opportunities for experience-oriented offerings, making RS application an effective way of assisting consumers in online decision-making. This study proposes a RS for movie lovers using neural networks in collaborative filtering systems for consumers’ experiential decisions. The experimental results reveal that it not only improves the accuracy of predicting movie ratings but also increases data transfer rates and provides richer user experiences.