From the course: Building Recommender Systems with Machine Learning and AI
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Exercise solution: Random exploration - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Exercise solution: Random exploration
- [Instructor] Hopefully you managed to get that running, but if you're new to Python, it may have been a little bit challenging. If you'd like to look at my solution, open up Spyder and close out everything you no longer want to look at with Control + Shift + W. Now open up everything in the Challenges folder of the course materials. Select the EvaluateUserCF-Exploration file. There are only a few changes here. First, we're importing MovieLens2 instead of MovieLens, which is my modified movie lens module that we'll look at shortly. It's been modified to identify movies from the most recent year. If we skip down to line 41, we can see that where I'm retrieving that list of new movies and defining which slot in my top 10 recommendations will be used to surface those new movies at random. Skipping down to line 78, this is where we are constructing the top 10 recommendations for each user. If we're in the final slot we defined, we choose one of those new movies at random. Otherwise, we…
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(Locked)
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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