Representing Temporal Knowledge for Case-Based Prediction
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
A Case-Based Song Scheduler for Group Customised Radio
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Content-based recommendation systems
The adaptive web
A taxonomy of sequential pattern mining algorithms
ACM Computing Surveys (CSUR)
Rule-Based impact propagation for trace replay
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
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People like creating their own videos by mixing various contents. Many applications allow us to generate video clips by merging different media like videos clips, photos, text and sounds. Some of these applications enable us to combine online content with our own resources. Given the large amount of content available, the problem is to quickly find content that truly meet our needs. This is when recommender systems come in. In this paper, we propose an approach for contextual video recommendations based on a Trace-Based Reasoning approach.