A Novel Recommendation Model Regularized with User Trust and Item Ratings

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A Novel Recommendation Model Regularized with User Trust and Item Ratings

A Novel Recommendation Model Regularized with User Trust and Item Ratings

Abstract

A Novel Recommendation Model Regularized with User Trust and Item Ratings Project report on Web mining We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance.

An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model.

Conclusion

A Novel Recommendation Model Regularized with User Trust and Item Ratings Project report on web mining This article proposed a novel trust-based matrix factorization model which incorporated both rating and trust information. Our analysis of trust in four real-world data sets indicated that trust and ratings were complementary to each other, and both pivotal for more accurate recommendations.

Our novel approach, TrustSVD, takes into account both the explicit and implicit influence of ratings and of trust information when predicting ratings of unknown items. Both the trust influence of trustees and trusters of active users are involved in our model.

 
Project Name A Novel Recommendation Model Regularized with User Trust and Item Ratings
Project Category Web mining and Security
Project Cost 65 $/ Rs 4999
Delivery Time 48 Hour
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