The Effectiveness of Collaborative Filtering-Based Recommendation Systems across Different Domains and Search Modes

Collaborative filtering (CF) is a personalization technology used by numerous e-commerce websites to generate recommendations for users based on others ’ evaluations. Although many studies have considered ways to refine CF algorithms, little is known about the effects of user and domain characteristics on the accuracy of CF systems. This study investigates the effects of two factors, domain and user search mode, on the accuracy of collaborative-filtering systems, using data collected from two different experiments ― one conducted in a consumer-product domain and one in a knowledge domain. The results show that the search mode employed by users strongly influences the accuracy of recommendations. CF works better when users are looking for specific information than when they are browsing out of general interest. Accuracy drops significantly when data from different search modes are mixed. The results also show that CF is more accurate in knowledge domains than in consumer-product domains. The study implies that CF systems in either domain will provide more accurate recommendations if they identify and accommodate users ’ search modes. I