From the course: Building Recommender Systems with Machine Learning and AI
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Temporal effects and value-aware recommendations - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Temporal effects and value-aware recommendations
- [Instructor] A topic that is really underrepresented in recommender system research is dealing with the effects of time. One example is seasonality. Some items like Christmas decorations only make for good recommendations just before Christmas. Recommending bikinis in the dead of winter, is also a bad idea. Picking up on annual patterns like this is hard to do, and most recommender systems won't do it automatically. As far as I know this is still an open problem in the research arena. Those of you looking for a master's research topic, there you go. But something you can do more easily and more generally, is taking the recency of a rating into account. Netflix in particular, found that these sorts of temporal dynamics are important. Your tastes change quickly, and a rating you made yesterday is a much stronger indication of your interest than a rating you made a year ago. By just weighting ratings by their age, using some sort of exponential decay, you can improve the quality of…
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Contents
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The cold start problem (and solutions)6m 12s
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Implement random exploration54s
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Exercise solution: Random exploration2m 18s
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Stoplists4m 48s
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Implement a stoplist32s
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Exercise solution: Implement a stoplist2m 22s
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Filter bubbles, trust, and outliers5m 39s
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Identify and eliminate outlier users44s
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Exercise solution: Outlier removal4m
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Fraud, the perils of clickstream, and international concerns4m 33s
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Temporal effects and value-aware recommendations3m 30s
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