Personalized Digital Television: Targeting Programs to by E.H. Chi

By E.H. Chi

Television audience at the present time are uncovered to overwhelming quantities of knowledge, and challenged through the plethora of interactive performance supplied by means of present set-top containers. to make sure extensive adoption of this know-how through shoppers, destiny electronic tv should take usability concerns completely under consideration. particularly, severe awareness needs to be paid to facilitate the choice of content material on a person foundation, and to supply easy-to-use interfaces that fulfill audience' interplay requirements.This quantity collects chosen examine reviews at the improvement of customized companies for Interactive television. Drawing upon contributions from academia and within the US, Europe and Asia, this booklet represents a complete photograph of cutting edge examine in custom-made tv.

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In: Proceedings AH’02 Workshop on Personalization in Future TV, Malaga, Spain, pp. 101^108. , Tomomune, Y. : 2004, Categorization of Japanese TV viewers based on program genres they watch. In this volume. Kurapati, K. : 2002, TV Personalization through Stereotypes. In: Proceedings AH’02 Workshop on Personalization in Future TV, Malaga, Spain, pp. 109^118. : Group modeling: selecting a sequence of television items to suit a group of users. In this volume. : 2002, IEEE Intelligent Systems: Information Customization, 17(6).

Particularly, Sinottica is a psychographic survey on: . Individuals (characteristics, values, behaviors, styles); . What they consume (products/goods/services and relative brands); . 2). By exploiting all these types of information, we could derive a set of stereotypes that partition the population in a precise way and re£ect viewing preferences. Notice that these studies are exploited to plan the presentation of commercials within TV programs by the most representative content providers. 7. Conclusions and Future Work This paper has presented the recommendation techniques applied in the Personal Program Guide (PPG).

2. 3. 4. 5. Implicit Implicit Implicit Implicit Explicit Bayesian based on individual view history Bayesian based on household view history Decision Tree based on individual view history Decision Tree based on household view history The individual and household view histories were used separately in order to determine whether a TV recommender could just do with one pro¢le per box in a household or if we needed to make ¢ne grain distinctions between individual household members. We developed two approaches to fusion.

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