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- 저자 : Fulian Yin (검색결과 3 건)
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A Content-Based Approach to Recommend TV Programs Enhanced with Delayering Tagging
Fulian Yin,Xingyi Pan,Huixin Liu,Jianping Chai 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.9
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.
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Analysis of Audience Interest and User Clustering Based on Program Tags
Fulian Yin,Xingyi Pan,Jianping Chai,Wenwen Zhang 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.11
This paper proposes an analysis method of user behavior to provide personalized program recommendation based on program tags in the field of broadcasting and television. Multidimensional Scaling Analysis is used to produce a quantitative description of viewing preferences. Hierarchical clustering is performed to determine the number of clusters, followed by K-means clustering to group the data according to audience interest in TV program tags. This divides the audience into groups with similar viewing preferences.
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Combination Weighting Method Based on Maximizing Deviations and Normalized Constraint Condition
Fulian Yin,Lu Lu,Jianping Chai,Yanbing Yang 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.2
When the weight of each attribute is determined in the multiple attribute decision making problems, calculated by the method of subjective values or objective values solely will cause the problem that weight coefficient is not reasonable. So the paper puts forward the weightingmethod which is based on maximizing deviations and normalized constraint condition. The method integrates the subjective and objective weighting information. On the one hand, the deviation between each weight vector which is determined by the various weighting method makes the maximum of its total deviation. On the other hand, the various evaluated object integrated value makes the maximum of its total evaluated value. Thus we establish a double objective optimization model. What’s more, we deduce the weight calculation formula by solving the model. Finally we have an experimental analysis. It proves that the combination weighting methodcan reflect the relative importance of each indicators and the information that index itself contains. In other words, it can reflect the subjective and objective decisions which make the weighting results more reasonable.
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