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Hot news recommendation system from heterogeneous websites based on bayesian model
Zhengyou Xia1, Shengwu Xu1, Ningzhong Liu1
1Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China.
Thescientificworldjournal
|August 6, 2014
Summary
This study introduces a Bayesian model for hot news recommendation from diverse sources, outperforming human experts. The model effectively identifies trending news for group customers, demonstrating its reliability and practical application.
Area of Science:
- Computer Science
- Information Retrieval
- Artificial Intelligence
Background:
- Existing news recommender systems primarily focus on single-source news, limiting their effectiveness with heterogeneous news sources.
- Personalized news services are crucial, but research on aggregating news from numerous diverse websites for group recommendations is limited.
Purpose of the Study:
- To develop a novel hot news recommendation model capable of processing news from hundreds of heterogeneous websites.
- To provide effective top hot news services for group customers, such as government staff.
Main Methods:
- A Bayesian model is proposed to determine hot news by calculating the joint probability of news items.
- The model integrates news from a large number of diverse online news sources.
- Performance is evaluated by comparing the model's recommendations against human expert judgments using real-world datasets.
Main Results:
- The proposed Bayesian model demonstrates reliability and effectiveness in identifying hot news from heterogeneous sources.
- Experimental results show the model's recommendations are comparable or superior to human expert evaluations.
- The model was successfully implemented in a real-world hot news recommendation system for the Hangzhou city government, yielding positive outcomes.
Conclusions:
- The developed Bayesian model offers a robust solution for hot news recommendation across multiple, diverse news platforms.
- This approach significantly advances the capability of news recommender systems to serve group customers with timely and relevant information.
- The successful implementation highlights the practical utility and scalability of the proposed hot news recommendation methodology.
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