2009 CollaborativeFilteringwithTempo

Subject Headings:

Notes

Developed further in (Koren, 2010)
Categories and Subject Descriptors: H.2.8 Database Management: Database Applications — Data Mining.
General Terms: Algorithms

Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics should be a key when designing recommender systems or general customer preference models. However, this raises unique challenges. Within the eco-system intersecting multiple products and customers, many different characteristics are shifting simultaneously, while many of them influence each other and often those shifts are delicate and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instance-decay approaches cannot work, as they lose too much signal when discarding data instances. A more sensitive approach is required, which can make better distinctions between transient effects and long term patterns. The paradigm we offer is creating a model tracking the time changing behavior throughout the life span of the data. This allows us to exploit the relevant components of all data instances, while discarding only what is modeled as being irrelevant. Accordingly, we revamp two leading collaborative filtering recommendation approaches. Evaluation is made on a large movie rating dataset by Netflix. Results are encouraging and better than those previously reported on this dataset.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 CollaborativeFilteringwithTempo	Yehuda Koren			Collaborative Filtering with Temporal Dynamics		KDD-2009 Proceedings		10.1145/1557019.1557072		2009