An Improved Recommendation Approach based on User's Standard and Interests

Abstract

Nowadays, the field of web personalization is growing exponentially. From e-mail, e-trading, and internet forums to social networking based websites, directly or indirectly utilize the concept of web personalization and recommendation system for providing customized services and attentiongrabbing offers to their users. By now, a wide range of recommendation systems have been proposed by various researchers and still the research is going on so as to attain the user's expectations. It is a general thought that a user should be recommended the best product from his/her favorite product categories. In this work, we show theoretically that a recommendation system that follows the above criterion does not recommend appropriate products to all types of users. In addition, we propose a user's standard based recommendation system that overcomes this limitation of conventional approach, and provides improved recommendations covering all types of users. For evaluation purpose, we have employed the KDD MovieLens dataset and developed a movie recommendation system based on the proposed approach. We introduce the term 'Gain' for measuring the difference created by the proposed approach as compared to conventional approach. Experimental results reveal that the proposed approach benefited 56% of users and improved 23% of total recommendations.

[Retracted] Optimization of the Hybrid Movie Recommendation System Based on Weighted Classification and User Collaborative Filtering Algorithm

“…With the increase of data, it is more and more difficult for people to find the information they really want from the massive data. At this time, the recommender system can play the maximum application value [1,2]. According to the user information and user historical behavior data, the recommendation system can accurately predict the user's preferences, personalize the things that users may be interested in, and greatly reduce the cost of finding target information.…”

Exploration on Scientific Research Data-Targeted Intelligent Recommendation System Using Machine Learning Under the Background of Sustainable Development

“…Tracking dynamics: personalized information recommendations should be based on quick and accurate acquisition of the users’ personalized information. First, it is necessary to conduct a real-time dynamic tracking analysis of the user’s behavior information to achieve accurate recommendations of the IRS ( Shukla et al, 2015 ). The framework of the proposed IRS is shown in Figure 3 .…”