Section 16 of 24 · 5 lessons

Recommendations and Ranking

Botie has more guides than a reader can inspect. We will work out which ones to suggest using popularity, descriptions, and shared preferences, then evaluate the short list the reader actually sees. Missing interactions and new readers need their own treatment.

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Choosing Useful Candidates

A reader should not have to search the whole shelf. Begin with what previous readers opened, then use guide descriptions when interaction history is missing.

  1. 01Recommending Something Before Knowing the ReaderBotie has a shelf of guides and a record of which were opened. Begin with a popularity baseline, then examine what those records actually tell us.
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  2. 02Matching a Reader’s Interests to Guide DescriptionsGuide descriptions give us evidence even before a guide has been opened. Represent topics explicitly and compare them with a reader’s interests.
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Learning From Interaction Patterns

Readers can share preferences that item descriptions miss. Keep missing ratings distinct from dislikes while learning from neighbours and compact factor representations.

  1. 03Learning From Readers With Similar PreferencesA guide’s description may miss what readers value. Shared ratings let Botie compare preferences, provided missing ratings remain distinct from dislikes.
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  2. 04Compressing a Sparse Table of PreferencesA rating table can be enormous and mostly empty. Small vectors for readers and guides can share information across its observed entries.
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Evaluating Recommendations

A useful guide needs to reach the part of the list someone sees. Evaluate the visible ranking and inspect readers and items with little history separately.

  1. 05Checking the List a Reader Actually SeesA recommender usually shows a short list. Evaluate that list’s relevant items, their positions, and the cases where little history is available.
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