• I.Edwin Albert
• Dr. Deepa A.J

An Enhanced Trust SVD model, trust-based matrix factorization technique is used for Recommendation system. Enhanced 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. By analysing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach Enhanced Trust SVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

Experimental, matrix factorization, SVD

"Enhanced Trust SVD: Recommendation Model with Explicit and Implicit Influence of User Trust and Ratings", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 12, page no.684-690, December 2019, Available :http://www.jetir.org/papers/JETIR1908100.pdf

"Enhanced Trust SVD: Recommendation Model with Explicit and Implicit Influence of User Trust and Ratings", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 12, page no. pp684-690, December 2019, Available at : http://www.jetir.org/papers/JETIR1908100.pdf