A system is seriously required for helping users to find their path on the shopping and entertainment web sites where the amounts of on-line information vastly increase. Therefore, recommender systems, new type of internet based software tool, appeared, and became an appealing subject for researchers. Collaborative filtering (CF) technique based on user is the one of the method widely used by recommender systems but they have some problems for waiting to be developed solutions that are more efficient. One of these mainly problems is data sparsity. While the number of products is increase, the ratio of common rated products is decrease so calculating the computations of neighbourhood become difficult. The other one is scalability which is the performance problem of the existing algorithms on the datasets has large amounts of information. In this article, we tackle these two questions: (1) how the data sparsity can be reduced ? (2) How to make recommendation algorithms more scalable? We present an approach to addressing the both of these problems at the same time by using a new CF model, constructed based on the Artificial Immune Network Algorithm (aiNet). It is chosen because aiNet is capable of reducing sparsity and providing the scalability of dataset via describing data structure, including their spatial distribution and cluster interrelations. The new user-item ratings dataset reduced by applying aiNet (aiNetDS) given more stable results and produced predictions more quickly than the raw user-item ratings dataset (rawDS). Besides, the effects of using clustering for forming the neighbourhoods to the system performance are investigated. For this, both of these dataset are clustered by using k-means algorithm and then these cluster partitions are used as neighbourhoods. As a result, it has been shown that the clustered aiNetDS is given more accurate and quick results than the others are.
By: A. Merve Acilar and Ahmet Arslan