원문정보
초록
영어
In response to explore how to extract the recommended items' features, a method is put forward called a Content-based TV Program Recommendation Approach Enhanced with Delayering Tagging. The Content-based approach is optimized to recommend TV programs and improved the way to extract the recommended items' features. Besides, the existing way of using supervised method to build user modeling is replaced with an unsupervised method using delayering tagging to show recommended TV program's content features and set up user preference model. After compared with Latent Factor Model and Collaborative Filtering recommendation algorithm with the same experimental data, the proposed algorithm in this paper increased the accuracy of 2.67\%, coverage rate of 3.02\% and 3.2\% of the Feature 1 value and achieved good recommendation results compared to the Latent Factor Model which revealed the best effect of recommendation.
목차
1. Introduction
2. Architecture of Content-Based TV Program Recommendation Approach Enhanced with Delayering Tagging
2.1. Architecture
2.2. Related Researches
3. Content-Based TV Program Recommendation Technology Improvedby Delayering Tagging Improvement
3.1. Traditional Recommendation Methods
3.2. CADT Recommendation Methods
4. Experiment and Result Analysis
4.1. Experiment 1: Recommend Model Based on Lingo Righteousness
4.2. Experiment 2: Collaborative Filtering Recommendation Based on Neighborhood
4.3. Experiment 3: Recommendation Method Contrast Experiment
5. Conclusion
Acknowledgements
References