Name
Abstract | ||
|---|---|---|
Due to the diversity of alternative programs to watch and the change of viewers' contexts, real-time prediction of viewers' preferences in certain circumstances becomes increasingly hard. However, most existing TV recommender systems used only current time and location in a heuristic way and ignore other contextual information on which viewers' preferences may depend. This paper proposes a probabilistic approach that incorporates contextual information in order to predict the relevance of TV contents. We consider several viewer's current context elements and integrate them into a probabilistic model. We conduct a comprehensive effectiveness evaluation on a real dataset crawled from Pinhole platform. Experimental results demonstrate that our model outperforms the other context-aware models. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/WI.2016.0046 | 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) |
Keywords | Field | DocType |
Context-based,TV-Recommender systems,Probabilistic model | Recommender system,Data mining,Contextual information,Heuristic,Information retrieval,Computer science,Context model,Statistical model,Probabilistic logic | Conference |
ISBN | Citations | PageRank |
978-1-5090-4471-9 | 0 | 0.34 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mariem Bambia | 1 | 0 | 0.34 |
| Mohand Boughanem | 2 | 923 | 109.00 |
| Rim Faiz | 3 | 98 | 36.23 |