Abstract
Recommender system (RS) has become a very important factor in many eCommerce sites. In our daily life, we rely on the recommendation from other persons either by word of mouth, recommendation letters, movie, item and book reviews printed in newspapers, etc. The typical Recommender Systems are software tools and techniques that provide support to people by identifying interesting products and services in online store. It also provides a recommendation for certain users who search for the recommendations. The most important open challenge in Collaborative filtering recommender system is the cold start problem. If the adequate or sufficient information is not available for a new item or users, the recommender system runs into the cold start problem. To increase the usefulness of collaborative recommender systems, it could be desirable to eliminate the challenge such as cold start problem. Revealing the community structures is crucial to understand and more important with the increasing popularity of online social networks. The community detection is a key issue in social network analysis in which nodes of the communities are tightly connected each other and loosely connected between other communities. Many algorithms like Givan-Newman algorithm, modularity maximization, leading eigenvector, walk trap, etc., are used to detect the communities in the networks. To test the community division is meaningful we define a quality function called modularity. Modularity is that the links within a community are higher than the expected links in those communities. In this paper, we try to give a solution to the cold-start problem based on community detection algorithm that extracts the community from the social networks and identifies the similar users on that network. Hence, within the proposed work several intrinsic details are taken as a rule of thumb to boost the results higher. Moreover, the simulation experiment was taken to solve the cold start problem.
Highlights
The goal of a Recommender System [1] is to generate meaningful recommendations to a collection of users for items and services that might interest them which is based on the Information Filtering (IF) Mechanism
The Item-Cold Start Problem occurs when a new item enters into the system; the new items appear frequently an e-commerce site
Since the new item is rated by a substantial number of users; the recommender system would not be able to recommend it to the users
Summary
The goal of a Recommender System [1] is to generate meaningful recommendations to a collection of users for items and services that might interest them which is based on the Information Filtering (IF) Mechanism. A recommender system equates few reference features from the user’s profile and looking forward to the rating that a user would give to an item they had not yet considered These features may be from the information item (Content-Based filtering approach) or the candidate’s social environment (Collaborative filtering approach). The main idea of Collaborative recommendation approaches [3] is to exploit information about the previous activities or opinions of an existing user community for predicting which items the current user of the system will most probably like or be interested in. Because of this collaborative filtering refers to people-to-people correlation [4].
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