An Item-based Collaborative Filtering Recommendation Algorithm Using Slope One Scheme Smoothing

• Published in: 2009 Second International Symposium on Electronic Commerce and Security Vol. 2; pp. 215 - 217
• Main Author: DeJia Zhang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2009
• Subjects: collaborative filtering; Collaborative work; Electronic commerce; Filtering algorithms; Information filtering; Information filters; International collaboration; Online Communities/Technical Collaboration; recommender system; Recommender systems; Security; slope one scheme; Smoothing methods; sparsity
• ISBN: 9780769536439; 0769536433
• DOI: 10.1109/ISECS.2009.173

Collaborative filtering is one of the most important technologies in electronic commerce. With the development of recommender systems, the magnitudes of users and items grow rapidly, resulted in the extreme sparsity of user rating data set. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of users' ratings is the major reason causing the poor quality. To address this issue, an item-based collaborative filtering recommendation algorithm using slope one scheme smoothing is presented. This approach predicts item ratings that users have not rated by the employ of slope one scheme, and then uses Pearson correlation similarity measurement to find the target items' neighbors, lastly produces the recommendations. The experiments are made on a common data set using different recommender algorithms. The results show that the proposed approach can improve the accuracy of the collaborative filtering recommender system.