A Group Profile Based Program Recommender for Interactive TV

• Main Authors: Yu-Wei Lee, 李育維
• Other Authors: Jiann-Jone Chen
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/60463946244249532172

碩士 === 國立臺灣科技大學 === 電機工程系 === 100 === Personalized recommender systems has been developed for applications such as online bookstore Barnes&Noble, online shopping mall Amazon, and multimedia programs. They are developed based on recorded user preferences to estimate user ratings on new items/stuffs. To recommend TV programs or movies, it has to perform recommendation for group users. By simply merging preferences of group users, it can act as a single user for recommendation. However, this approach ignores individual preferences and user dominance in interaction, which cannot reflect practical group users’ preferences. We proposed to estimate inter-user dominance factor through the neural network algorithm, based on practical group user rating records. In addition, both content-based and user-based collaborative filtering algorithms are adopted based on the inter-user dominant factors to predict group users’ preference for program recommendation. The proposed adaptive program recommender based on group user profiles is evaluated from practical experiments on Movielens user rating databases. In addition, an active face recognition module has been integrated with the recommender system to provide touchless and user-friendly interface for a family TV program recommendation prototype. Experiments showed that the proposed method can achieve higher accuracy in recommending video programs for group users, in additional to user-friendly recommendation function.