دانلود رایگان مقاله به کار گیری سیستم های ایمنی مصنوعی با فیلترینگ مشترک برای سفارش فیلم

Abstract

Collaborative filtering is a widely used recommendation technique and many collaborative filtering techniques have been developed, each with its own merits and drawbacks. In this study, we apply an artificial immune network to collaborative filtering for movie recommendation. We propose new formulas in calculating the affinity between an antigen and an antibody and the affinity of an antigen to an immune network. In addition, a modified similarity estimation formula based on the Pearson correlation coefficient is also developed. A series of experiments based on MovieLens and EachMovie datasets are conducted, and the results are very encouraging.

5. Conclusion

In this paper, we presented an AIS collaborative filtering system for rating prediction and recommendation. We employed an artificial immune algorithm to train a set of immune networks. The rating data was treated as antigens, and a number of immune networks were generated by copying the antigens as the antibodies of the immune networks. These immune networks were then used as the basis for finding the nearest neighbors for a target user or item. A revised Pearson correlation coefficient was also introduced in this paper, and its effectiveness was confirmed experimentally. A prediction formula based on the generated immune networks was also devised, and the performance of our AIS collaborative filtering system using this prediction formula was evaluated. The results are encouraging, as the performance of our system is comparable to some state of the art techniques in terms of mean absolute error. In addition to mean absolute error, the precision and recall of our system on some well known datasets was also evaluated. Our system produces very high precision and recall for these datasets. Thus, if the movie company can understand or predict what movie the customers need in advance, the company can adopt more effective marketing strategy to the customers