Profile and Rating Similarity Analysis for Recommendation Systems Using Deep Learning

Recommendation systems are going to be an integral part of any E-Business in near future.As in any other E-business,recommendation systems also play a key role in the travel business where the user has to be recommended with a restaurant that best suits him.In general,the recommendations to a user are made based on similarity that exists between the intended user and the other users.This similarity can be calculated either based on the similarity between the user profiles or the similarity between the ratings made by the users.First phase of this work concentrates on experimentally analyzing both these models and get a deep insight of these models.With the lessons learned from the insights,second phase of the work concentrates on developing a deep learning model.The model does not depend on the other user's profile or rating made by them.The model is tested with a small restaurant dataset and the model can predict whether a user likes the restaurant or not.The model is trained with different users and their rating.The system learns from it and in order to predict whether a new user likes or not a restaurant that he/she has not visited earlier,all the data the trained model needed is the rating made by the same user for different restaurants.The model is deployed in a cloud environment in order to extend it to be more realistic product in future.Result evaluated with dataset,it achieves 74.6%is accurate prediction of results,where as existing techniques achieves only 64%.

Improved Collaborative Filtering Recommendation Based on Classification and User Trust

When dealing with the ratings from users,traditional collaborative filtering algorithms do not consider the credibility of rating data,which affects the accuracy of similarity.To address this issue,the paper proposes an improved algorithm based on classification and user trust.It firstly classifies all the ratings by the categories of items.And then,for each category,it evaluates the trustworthy degree of each user on the category and imposes the degree on the ratings of the user.Finally,the algorithm explores the similarities between users,finds the nearest neighbors,and makes recommendations within each category.Simulations show that the improved algorithm outperforms the traditional collaborative filtering algorithms and enhances the accuracy of recommendation.