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Advisor(s)
Abstract(s)
This paper describes a forgetting technique for the live update of viewer profiles based on individual sliding windows, fading and incremental matrix factorization. The individual sliding window maintains, for each viewer, a queue holding the last n viewer ratings. As new viewer events occur, they are inserted in the viewer queue, by shifting and fading the queue ratings, and the viewer latent model is faded. We explored time, rating-and-position and popularity-based fading techniques, using the latter as the base fading algorithm. This approach attempts to address the problem of dynamic viewer profile updating (volatile preferences) as well as the problem of bounded processing resources (fixed size queues). The results show that our approach outperforms previous approaches, improving the quality of the predictions.
Description
Keywords
Forgetting Technique Stream Mining Fading Strategies
Pedagogical Context
Citation
Publisher
ACM Press