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Data Prediction Based on User Preference

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

Recommender systems are admittedly the widely used applications over the internet E commerce sites and thus the success of a recommender system depends on the time and accuracy of the results returned in response to information supplied by the users.Now-a-days, many big E commerce systems and even social networking portals provide the facility of recommendation on their sites, thus underscoring the demand for effective and accurate recommendation system. But still most of the recommender systems suffer from the problem of cold start, sparsity and popularity bias of the provided recommendation. Also, these recommender systems are unable to provide the recommendation to someone with unique taste. This paper describes a preference based recommender system and collaborative filtering approach which is used to solve prediction and recommendation problem. The collaborative filtering aims at learning predictive model of user interests, behavior from community data or user preferences. It describes a family of model-based approaches designed for this task. Probabilistic latent semantic analysis (PLSA) was presented in the context of data retrieval or text analysis area.This work can be used to predict user ratings in the recommendation system context. It is based on a statistical modeling technique which introduces a latent class of variables in a mixture model setting to discover prototypical interest profiles and user communities. The main advantages of PLSA over memory-based methods are an explicit, constant time prediction and more accurate compact model. It can be used to mine for the user community. The experimental results show substantial improvement in prediction accuracy over existing methods.

Keywords

Recommender systems Information Search and Retrieval Information filtering Probabilistic Latent Semantic Analysis Collaborative Filtering mixture models latent semantic analysis machine learning 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Basic ScienceIndian Institute of Technology MandiMandiIndia
  2. 2.Hindustan College of Science and TechnologyMathuraIndia

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