An Enhanced Content-Based Recommender System for Academic Social Networks

Abstract

The present study utilizes social computing techniques to enhance the content-based recommender systems. Coined as Enhanced Content-based Algorithm using Social Networking (ECSN), this recommender algorithm is applied in academic social networks to suggest the most relevant items to members of these online societies. In addition to considering user's own preferences, ECSN takes advantage of the interest and preferences of user's friends and faculty mates for providing more accurate recommendations. The research experiments were conducted by applying four different algorithms - random, collaborative, content-based, and ECSN, for 14 consecutive weeks. During this period, 1398 academic items were recommended to all 920 members of Malaysian Experts Academic Social Network (MyExpert). ANOVA tests indicate that the proposed algorithm significantly improves the prediction accuracy of algorithms based on well-known measurements of precision, fallout and F1. It is believed that this study can make a significant contribution to the level of user satisfaction in academic social networks.