A hybrid recommender system using trust and bi-clustering in order to increase the efficiency of collaborative filtering

In the present era, the amount of information grows exponentially. So, finding the required information among the mass of information has become a major challenge. The success of e-commerce systems and online business transactions depend greatly on the effective design of products recommender mechanism. Providing high quality recommendations is important for e-commerce systems to assist users in making effective selection decisions from a plethora of choices. Recommender systems have been developed in order to respond this problem in order to customize the required information for users.
So far, several types of recommender systems have been developed such as collaborative filtering recommender systems, content-based recommender systems and knowledge-based recommender systems. Each of these systems has advantages and disadvantages. Most of the recommender systems are based on collaborative filtering; Collaborative filtering is a widely accepted technique to generate recommendations based on the ratings of like-minded users. In fact, the main idea of this technique is to benefit from the past behavior or existing beliefs of the user community to predict products that are likely to be liked by the current user of the system. In collaborative filtering, we use the similarity between users or items to recommend products. However, this technique has several inherent problems such as cold start, sparsity and scalability.
Since the collaborative filtering system is considered to be the most widely used recommender system, solving these problems and improving the effectiveness of collaborative filtering is one of the challenges raised in this context. None of the proposed hybrid systems have ever been able to resolve all of the collaborative filtering problems in a single and desirable manner; in this paper, we proposed a new hybrid recommender system that applies trust network as well as bi-clustering to improve the effectiveness of collaborative filtering. Therefore, the objectives of this research can be summarized as follows: sparsity reduction, increasing the speed of producing recommendations and increasing the accuracy of recommendations.
In the proposed system, the trust between users is used to fill the user-item matrix which is a sparse matrix to solve the existing problem of sparsity. Then using bi-clustering, the user-item matrix is subdivided into matrices to solve the problem of scalability of the collaborative filtering and then the collaborative filtering is implemented for each sub matrix and the results from the implementation of the collaborative filtering for the sub-matrices are combined and recommendations are made for the users.
The experimental results on a subset of the extended Epinions dataset verify the effectiveness and efficiency of our proposed system over user-based collaborative filtering and hybrid collaborative filtering with trust techniques.
Improve sparsity problem
Experimental results showed that our proposed system solves some of the sparsity problems which is due to the using the trust in the hybrid recommender system. By using trust, we can predict many uncertain ratings. Thus, transforming the user-item sparsity matrix into a half-full matrix.
Improve scalability problem
The results show that the proposed system has a higher speed compared with the user-based collaborative filtering algorithm and hybrid collaborative filtering with trust, and increasing the volume of data has little effect on increase online computing time. The reason can be summarized as a using of bi-clustering. Bi-directional clusters are made offline and break down the matrix of rankings into smaller subsets. Implementing the collaborative filtering on these smaller sets has led to increased computing speed.
Improve the new user problem
This system can provide accurate results for the new users due to the use of trust, because product collections viewed by new user can increase with the trust between the users. This system can predict the similarity between the new user and other users. So, the results are more accurate than the results of the user-based collaborative filtering and hybrid collaborative filtering with trust.