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.
Read or Download Personalized Digital Television: Targeting Programs to Individual Viewers PDF
Similar organization and data processing books
Television audience at the present time are uncovered to overwhelming quantities of data, and challenged through the plethora of interactive performance supplied via present set-top bins. to make sure large adoption of this expertise by means of shoppers, destiny electronic tv must take usability concerns completely under consideration.
This booklet constitutes the completely refereed prolonged postproceedings of the sixth overseas Workshop on Membrane Computing, WMC 2005, held in Vienna, Austria, in July 2005. The 20 revised complete papers offered including five invited papers went via rounds of reviewing and development. The papers during this quantity conceal the entire major instructions of study in membrane computing, starting from theoretical themes in arithmetic and laptop technology, to program matters, specially in biology.
Computing on the fringe of Nature -- Rethinking desktops -- Shrinking expertise -- A Peek Into Quantumland -- The Qubit: final 0 and One -- Are Bits using Us Bankrupt? -- Quantum Computing -- tips of the exchange -- Quantum reminiscence Registers -- The prepare--evolve--measure Cycle -- Quantum Gates and Quantum Circuits -- instance of a Quantum Computation -- What Can desktops Do?
- Bioinformatics: Data, Sequence Analysis and Evolution
- Circular binary segmentation for the analysis of array-based DNA copy number data
- Languages and Compilers for Parallel Computing: 16th International Workshop, LCPC 2003, College Station, TX, USA, October 2-4, 2003. Revised Papers
- Distortion-Free Data Embedding for Images
- Notes on Germanium Oxide
- Collaboration between Human and Artificial Societies: Coordination and Agent-Based Distributed Computing
Additional resources for Personalized Digital Television: Targeting Programs to Individual Viewers
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.