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Top2. Recommender Systems
Nowadays, mass of information is increasing faster than our power of processing information. This information is generated as result of producing new books, articles, issues, and so on every year. The largest e-commerce companies offer millions of items for sale. This is a challenge for users, because it is difficult for them to select their required items among the great mass of items. As a result, there is a need for automated recommendation methods. Recommender systems have emerged for this purpose. They include methods for finding important information and knowledge from large datasets with minimal user involvement (Shaw et al., 2009; Schiaffino & Amandi, 2009; Shani et al., 2005).
Recommender systems have recently gained more attention because of their importance for e-commerce applications (Lin, 2000). They tend to understand users’ interests, using their history of buying behavior and suggest items that might interest them (Shaw et al., 2009).
There are different definitions for recommender systems. A recommender system is defined as an information system that is able to analyze past behaviors and present recommendations for current problems1. In the other hand, recommender systems use methods predicting thinking of users, to recognize the most appropriate and nearest item to his/her favorite and then suggest that item to him. In fact, these systems simulate the process that we utilize in our routine life and execute it automatically. Since, in our routine life, we try to find someone nearest to our favorite and ask them about our choices. Many of e-commerce companies use this method and use such services for retaining existing customers and also finding new customers. Hitherto, very different presentation and selling services are being introduced to customers. Presentation personalization systems and item recommendation systems are examples that many e-commerce companies use in order to retain existing customers and find new ones (Akbari, 2008).