A Personalized Online News Recommendation System
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10.5120/9212-3758 |
Saranya.k.g and Sudha G Sadhasivam. Article: A Personalized Online News Recommendation System. International Journal of Computer Applications 57(18):6-14, November 2012. Full text available. BibTeX
@article{key:article,
author = {Saranya.k.g and G. Sudha Sadhasivam},
title = {Article: A Personalized Online News Recommendation System},
journal = {International Journal of Computer Applications},
year = {2012},
volume = {57},
number = {18},
pages = {6-14},
month = {November},
note = {Full text available}
}
Abstract
Traditional news recommendation systems strive to adapt their services to individual users by virtue of both user and news context information. This paper describes personalized news recommendation approach based on dynamic updating policy and collaborative filtering. Adaptive user profiling is a principled framework for news selection based on the intrinsic property of user interest presented, with a good balance between the novelty and diversity of the recommendation result. Also it considers the exclusive characteristics like news context, access patterns, popularity of the news and recency. Collaborative filtering approach can efficiently capture user's behavior in case where the overlap in historical assumptions across users in relatively high and the context universe is almost static. The major issue with the personalized news recommendation system is scalability. This paper addresses the above mentioned issue with the help of hadoop framework. Experiments on a collection of sports related news obtained from various news websites demonstrate the efficiency of the proposed approach.
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