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Final year IEEE projects 2016 based on Java Cloud Computing
  • Final year IEEE projects 2016 based on Java Cloud Computing

A Novel Recommendation Model Regularized with User Trust and Item Ratings

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Novel Recommendation Model Regularized User Trust, Item Ratings

Based on the recommendations model, a trust-based matrix factorization technique called TrustSVD is proposed.

TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparse and cold start problems and their degradation of recommendation performance.Novel Recommendation Model Regularized User Trust, Item Ratings

Explicit and implicit influence of both ratings and trust should be taken into consideration in a recommendation model during the analysis of social trust data from four real-world data sets.Novel Recommendation Model Regularized User Trust, Item Ratings. By incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user, TrustSVD builds on top of a state-of-the-art recommendation algorithm, SVD++ which uses the explicit and implicit influence of rated items.

The proposed technique is the first to extend SVD++ with social trust information. TrustSVD achieves better accuracy than other ten counterparts recommendation techniques based on the experiment carried out on four data sets.

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