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Rating Prediction by Combining User Interest and Friendly Relationship

  • H. P. AmbulgekarEmail author
  • Manesh B. Kokare
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 985)

Abstract

Due to the popularity of social media, users of these sites are sharing what they’re doing with their friends within numerous social sites. Now, we’ve an enormous quantity of explanations, ratings and comments for native facilities. The data is effective for fresh users to evaluate whether or not the facilities meet their needs before its use. During this paper, suggest an approach for rating prediction by combining user interest, friendly relationship info along with item reputation factor to improve the prediction accuracy. So as to predict ratings, we tend to concentrate on users’ rating behaviors and reputation similarity between items. Within the proposed approach for rating prediction, we tend to fuse five factors like personal interest (items and user’s topics related), similarity of social interest (user interest related), social rating behavior similarity (users’ rating pattern habits related), social rating pattern diffusion (behavior of users diffusions related), and item similarity, this can be deduced by distributing the rating of a user set that represent customers evaluation—into a combined framework of matrix factorization. We tend to perform a number of experiments with the Yelp dataset. Figures in the results show that our approach is having good performance.

Keywords

Data mining Recommender system Social user behavior Collaborative filtering 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSGGS IE & TNandedIndia
  2. 2.Department of Electronics and Telecommunication EngineeringSGGS IE & TNandedIndia

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