Personalized Recommendation Algorithms with Collaborative Filtering
Murali Mohan Thomurthy · Hiroshima University Acedemic Information Repository (Hiroshima University) · 2015
An explosive growth of enormous information on the web, created the universe as global village.It is a big problem for getting the relevant information from the internet.Personalized Recommendation Systems may be used to get relevant information from the internet.Recommender System is to generate significant recommendations to a collection of users for items or products that might interest them.This is a powerful new technology for extracting additional value for a business from its user databases and help users find items they want to buy from a business.Real world examples for the recommender systems are amazon.com(for books) and netflix.com(for movies).Collaborative filtering is one of the important techniques in personalized recommendation systems and predicting the interests of a user by collecting preference information from many users.The Collaborative Filtering models can also hold with the situations where user profiles are supplied by observing user interactions with a system and dealt with user profiles that are obtained by requesting users to rate information items.Broadly, they are classified into (i) Memory-based Collaborative Filtering techniques such as the user-based, item-based and neighborhood-based Collaborative filtering algorithm (ii) Model-based Collaborative Filtering techniques such as Bayesian belief nets, Clustering, Singular Value Decomposition (SVD) and MDP-based Collaborative filtering and (iii) Hybrid Collaborative filtering techniques such as the Content-boosted Collaborative Filtering and Personality Diagnosis.In this thesis, a comprehensive study has been involved to process huge data sets and uses the popular collaborative filtering algorithms as the basis for proposed modifications.In the beginning, the problem of inaccurate finding and falling recommendation quality of the prediction will bring forth.Then, the user's interest words will be collected to build the user interest model.Finally, modified algorithms have been proposed, they are 1) User based Collaborative Filtering based on Pearson Correlation which is Memory-based technique, 2) Singular Value Decomposition based on Composite Prototypes which is Model-based technique and 3) Hybrid Collaborative Filtering based on the predictions-probabilistic prototype.The experimentation is done with MovieLens dataset which is available for research purpose provided by the GroupLens Research Project agency at the University of Minnesota.The measured Mean Absolute Error (MAE) of the proposed model are compared with available models from literature and finally the performance analysis is done based on parameter MAE.The comparative analysis and comprehensive study shows that Content-boosted Collaborative Filtering algorithm puts forward for better performance among the other comparative algorithms and hence, feasible solutions will be obtained using Content-boosted Collaborative Filtering recommendation methods instead of other recommendation methods.