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Semantic user profiling techniques for personalised multimedia recommendation
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PRE
AI
DOI：10.1007/s00530-010-0189-6.png)
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摘要
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Due to the explosion of news materials available through broadcast and other channels, there is an increasing need for personalised news video retrieval. In this work, we introduce a semantic-based user modelling technique to capture users' evolving information needs. Our approach exploits implicit user interaction to capture long-term user interests in a profile. The organised interests are used to retrieve and recommend news stories to the users. In this paper, we exploit the Linked Open Data Cloud to identify similar news stories that match the users' interest. We evaluate various recommendation parameters by introducing a simulation-based evaluation scheme.
Keyword:
Long-term user profiling
Video annotation
Multimedia recommendation
Evaluation
User simulation
Semantic web technologies
期刊
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3.1
论文数:
2.8K
被引数:
2.7K
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