From the course: Building Recommender Systems with Machine Learning and AI
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Coverage, diversity, and novelty - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Coverage, diversity, and novelty
- [Instructor] Accuracy isn't the only thing that matters with recommender systems. There are other things we can measure if they're important to us. For example, coverage. That's just the percentage of possible recommendations that your system is able to provide. Think about the movie lens data set of movie ratings we're using in this course. It contains ratings for several thousand movies, but there are plenty of movies in existence that it doesn't have ratings for. If you were using this data to generate recommendations on say, IMDB, then the coverage of this recommender system would be low, because IMDB has millions of movies in its catalog, not thousands. It's worth noting that coverage can be at odds with accuracy. If you enforce a higher quality threshold on the recommendations you make, then you might improve your accuracy at the expense of coverage. Finding the balance of where exactly you're better off recommending nothing at all can be delicate. Coverage can also be…
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Train/test and cross-validation3m 49s
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Accuracy metrics (RMSE and MAE)4m 6s
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Top-N hit rate: Many ways4m 35s
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Coverage, diversity, and novelty4m 55s
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Churn, responsiveness, and A/B tests5m 6s
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Review ways to measure your recommender2m 55s
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Walkthrough of RecommenderMetrics.py6m 53s
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Walkthrough of TestMetrics.py5m 8s
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Measure the performance of SVD recommendations2m 24s
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