Recent Papers

We propose Trust SVD, a trust-based matrix factorization technique for recommendations. Trust SVD integrates multiple information sources into the recommendation model in order to reduce the data 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. Trust SVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the 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 SVD++ with social trust information. Experimental results on the four data sets demonstrate that Trust SVD achieves better accuracy than other ten counterparts recommendation techniques. In this project, we propose Trust SVD, a trust-based matrix factorization technique for recommendations. Trust SVD integrates multiple information sources into the recommendation model in order to reduce the data 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. Trust SVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the 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 SVD++ with social trust information.

Copyright © 2023 Saveetha P, Latha S, Sakthisri S S, Suriyakala B. This is an open access article distributed under the Creative Commons Attribution License.