Abstract
Lots of people employ recommender systems to diminish the information overload over the internet. This leads the user in a personalized manner to hit upon interesting or helpful objects in a huge space of possible options. Amongst different techniques, Collaborative filtering recommender system has pulled off great success. But this technique pays no heed towards the social relationship of the users. This problem gave birth to the Social recommender system technology which possesses the capability to recognize users likings and preferences and their social relationships. In this paper, we present novel method where we combine collaborative filtering recommender system with social friend network to use social relationships. For this, we have made use of data related to users which provides their interests as well as their social relationship. Our method helps to find the friends with dissimilar tastes and determine the close friends amongst direct friends of targeted user which has more similar tastes. This proposed approach resulted in more precise and realistic results than traditional system.
Highlights
INTRODUCTIONE-Commerce has expanded in a large degree. So, Recommender Systems turn out to be a great way of harvesting and supplying customers to the business which helps for boosting the sell in the market
In last few decades, e-Commerce has expanded in a large degree
Recommender Systems turn out to be a great way of harvesting and supplying customers to the business which helps for boosting the sell in the market
Summary
E-Commerce has expanded in a large degree. So, Recommender Systems turn out to be a great way of harvesting and supplying customers to the business which helps for boosting the sell in the market. Traditional collaborative filtering is not much effective to generate recommendation, because it thinks about the similarity of taste between different users available in user-item dataset These users can be the strangers in real life. People tend to spend their lots of time over them for surfing, chatting, giving their opinions on different issues, etc It became the large source for collecting information about users. Top nearest neighbors are found out from traditional collaborative filtering technique At last, those ratings of close friends and neighbors are combined to predict the recommendation for targeted user. Those ratings of close friends and neighbors are combined to predict the recommendation for targeted user The performance of this approach is evaluated in terms of Mean Absolute Error and Root Mean Square Error by comparing it with traditional and social recommender system.
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