Code:-
JPJA2377
Abstract:-
We propose TrustSVD, a trust-based matrix factorization technique for recommendations. Trust SVD 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. TrustSVD 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.
Existing System:-
Disadvantages of Existing System:-
Existing trust-based models may not work well if there exists only trust-alike relationships.</li> <li>These observations could other kinds of recommendation problems.</li> <li>Existing trust based models consider only the explicit influence of ratings.</li> <li>The utility of ratings is not well exploited.</li> <li>Existing trust-based models do not consider the explicit and implicit influence of trusts imultaneously.</li>
Proposed System:-
We propose a novel trust-based recommendation model regularized with user trust and item ratings, termed as Trust SVD.</li> <li>Our approach builds on top of a stateof-the-art model SVD++ through which both the explicit and implicit influence of user-item ratings are involved to generate predictions. In addition, we further consider the influence of trust users (including trustees and trusters) on the rating prediction for an active user.</li> <li>This ensures that user specific vectors can be learned from their trust information even if a few or no ratings are given. In this way, the concerned issues can be better alleviated.</li> <li>Therefore, both explicit and implicit influences of item ratings and user trust have been considered in our model, indicating its novelty. In addition, a weighted-regularization technique is used to help avoid over-fitting for model learning.</li> <li>The experimental results on the data sets demonstrate that our approach works significantly better than other trust-based counterparts as well as other ratings-only high-performing models (ten approaches in total) in terms of predictive accuracy, and is more capable of coping with the cold-start situations.</li> <li>There are two main recommendation tasks in recommender systems, namely item recommendation and rating prediction. Most algorithmic approaches are only (or best) designed for either one of the recommendations tasks, and our work focus on the rating prediction task.</li>
Advantages of Proposed System:-
Our first contribution is to conduct an empirical trust analysis and observe that trust and ratings can complement to each other, and that users may be strongly or weakly correlated with each other according to different types of social relationships.</li> <li>These observations motivate us to consider both explicit and implicit influence of ratings and trust into our trust-based model.</li> <li>Potentially, these observations could be also beneficial for solving other kinds of recommendation problems, e.g., top-N item recommendation.</li>
Hardware Requirements:-
Software Requirements:-
- Operating system : Windows 7.</li> <li style="text-align: justify;">Coding Language : JAVA/J2EE</li> <li style="text-align: justify;">Tool : Netbeans 7.2.1</li> <li style="text-align: justify;">Database : MYSQL</li>
Cost:-
Rs 2000
Online Book Recommendation System
Code:
JPJA2377
Abstract:
We propose TrustSVD, a trust-based matrix factorization technique for recommendations. Trust SVD 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. TrustSVD 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.
Existing System:
Disadvantages of Existing System:
Existing trust-based models may not work well if there exists only trust-alike relationships.</li> <li>These observations could other kinds of recommendation problems.</li> <li>Existing trust based models consider only the explicit influence of ratings.</li> <li>The utility of ratings is not well exploited.</li> <li>Existing trust-based models do not consider the explicit and implicit influence of trusts imultaneously.</li>
Proposed System:
We propose a novel trust-based recommendation model regularized with user trust and item ratings, termed as Trust SVD.</li> <li>Our approach builds on top of a stateof-the-art model SVD++ through which both the explicit and implicit influence of user-item ratings are involved to generate predictions. In addition, we further consider the influence of trust users (including trustees and trusters) on the rating prediction for an active user.</li> <li>This ensures that user specific vectors can be learned from their trust information even if a few or no ratings are given. In this way, the concerned issues can be better alleviated.</li> <li>Therefore, both explicit and implicit influences of item ratings and user trust have been considered in our model, indicating its novelty. In addition, a weighted-regularization technique is used to help avoid over-fitting for model learning.</li> <li>The experimental results on the data sets demonstrate that our approach works significantly better than other trust-based counterparts as well as other ratings-only high-performing models (ten approaches in total) in terms of predictive accuracy, and is more capable of coping with the cold-start situations.</li> <li>There are two main recommendation tasks in recommender systems, namely item recommendation and rating prediction. Most algorithmic approaches are only (or best) designed for either one of the recommendations tasks, and our work focus on the rating prediction task.</li>
Advantages of Proposed System:
Our first contribution is to conduct an empirical trust analysis and observe that trust and ratings can complement to each other, and that users may be strongly or weakly correlated with each other according to different types of social relationships.</li> <li>These observations motivate us to consider both explicit and implicit influence of ratings and trust into our trust-based model.</li> <li>Potentially, these observations could be also beneficial for solving other kinds of recommendation problems, e.g., top-N item recommendation.</li>
Hardware Requirements:
Software Requirements:
- Operating system : Windows 7.</li> <li style="text-align: justify;">Coding Language : JAVA/J2EE</li> <li style="text-align: justify;">Tool : Netbeans 7.2.1</li> <li style="text-align: justify;">Database : MYSQL</li>
Cost:
Rs 2000
Additional Information
Tools Used:
Java
Cost:
₹Rs 2000
