User-based collaborative filtering , a widely used nearest neighbour-based recommendation technique, predicts an item’s rating by aggregating its ratings from similar users. User similarity is traditionally calculated by cosine similarity or the Pearson correlation coefficient . However, both of these measures consider only the direction of rating vectors, and suffer from a range of drawbacks. To overcome these issues, we propose a novel Bayesian similarity measure based on the Dirichlet distribution, taking into consideration both the direction and length of rating vectors. We posit that not all the rating pairs should be equally counted in order to accurately model user correlation. Three different evidence factors are designed to compute the weights of rating pairs. Further, our principled method reduces correlation due to chance and potential system bias. Experimental results on six real-world datasets show that our method achieves superior accuracy in comparison with counterparts.

Recommendation is an opinion given by an analyst to his/her client whether the given stock is worth buying or a particular place is worth visiting or not. They use various projections as a basis for issuing recommendations. Item rating is a group of classifications designed to extract information about a quantitative or qualitative attribute. Here we use a scale to reflect the quality of product where user selects the number which is taken into consideration. In order to enhance the novel recommendation model, we propose a trust based recommendation model with item rating where data sparsity and cold start problem are rectified.We make use of personalized social networking to connect people in a commodity so that people can get to know about a product or place in detail by the information shared about it and the user can sort out things according to their needs and specification.

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Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree of effectiveness in improving recommendation performance. This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information. Specifically, we provide an overview of state-of-the-art recommendation algorithms with side information from two orthogonal perspectives. One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning and deep learning models. The others cover different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and non-structural data (text, image and video features). Finally, we discuss challenges and provide new potential directions in recommendation, along with the conclusion of this survey.