Recommendation Top-N with the approach of Novel Rank

Abstract

A Novel trust based recommendation model, which is regularized with user trust and item rating is Trust SVD. Our method is novel for its consideration  of both the explicit (rating based on social circle) and implicit influence (self rating) of item ratings and of  the user trust. In addition, a weighted regularization  technique is used to avoid over fitting for model earning. This trust based matrix factorization model  incorporates both rating and trust information for  rating prediction. Trust information is very sparse, yet complementary to the information. Thus, focusing  too much on either one kind of information achieves only marginal gains in predictive correctness.Also users are strongly correlated with their trust neighbors and have a weakly positive correlation with their trust alike neighbors (e.g., friends). These observations are motivated to consider both explicit and implicit influence of ratings and of trust in a trust based model. A weighted λ regularization technique was used to regularize the user and item  specific latent feature vectors. This guarantees that  the user specific vectors can be learned from their  trust information even if a few or no ratings are  given. So data sparsity and cold start issues for

recommendation can be solved. Trust SVD can outperform both trust and ratings based methods in the predictive accuracy. Recommender systems employ from a specific type of information filtering system technique that attempts to recommend information items (movies, TV program/show/episode, video on demand, web pages, books, news, music, images, scientific literature etc.) or social elements (e.g. people, events or groups) that are likely to be of interest to the user. Typically, a recommender system approximates a user profile to some reference characteristics, and tries to predict the 'rating' or 'preference' that a user would give to an item. These characteristics maybe from the information item which may be similar(the content based approach)  or the user's social surrounding (the  collaborative filtering). The recommender system  applies Data Mining (DM) approaches and prediction algorithms to predict  user’s interest on fact, product and services. However, most of these systems can  bear in their core an algorithm that can be used to understand a particular case of a Data Mining (DM) technique.  The process of data mining consists of 3 steps: Data Preprocessing, Data Analysis and Result  Interpretation. Examples of recommender system are amazon.com,eBay, snapdeal.com