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Efficient Model to Incorporate Rating and Trust Data for Recommendation


Nedunuri Jasmine Nirmala Varsha , Jawaharlal Nehru Technological University, UCEK, Kakinada; A. Krishna Mohan, Jawaharlal Nehru Technolgical University, UCEK, Kakinada


Social Trust, Implicit Ratings, CF, Recommender Systems


Propose a novel Domain-sensitive Recommendation algorithm assisted with assigned user item subgroup analysis, which integrates rating prediction and domain detection into a unified framework. A Trust SVD model, trust based matrix factorization technique is used for Recommendation system. Trust SVD (Singular Value Decomposition) integrates multiple information sources into the recommendation model in order to reduce well known problems Data Sparsity and Cold start Problems and their degradation recommendation performance. An Analysis of social trust data from four real-world data sets suggests only explicit ratings and doesn't specifies implicit rating values. In this Recommendation model both Explicit and implicit rating values are taken into consideration. Trust SVD model therefore builds on top of State-of-the-art algorithm, Trust SVD++ (which uses explicit and implicit influence of item ratings), by further incorporating both explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend Trust SVD++ with social trust information. Here Propose a Trust SVD model to learn a trust aware personalized ranking method with multi-faceted trust relations for implicit feedback. It reconstruct the observed rating data with learned latent factor representations of both users and items, with which those unobserved ratings to users and items can be predicted directly. Recommender systems have been widely used to provide users with high-quality personalized recommendations from large volume of choices. Robust and accurate recommendations are important in e-commerce operations.

Other Details

Paper ID: IJSRDV6I60148
Published in: Volume : 6, Issue : 6
Publication Date: 01/09/2018
Page(s): 259-264

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