Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Inside Recommendation Engines: Ranking signals, feedback loops, fairness, and user choice

Inside Recommendation Engines: Ranking signals, feedback loops, fairness, and user choice

Hai, Vo

Paperback

DatabasesGeneral Computers

ISBN13: 9798174033498
Publisher: Independently Published
Published: Sep 13 2026
Pages: 202
Weight: 0.61
Height: 0.43 Width: 6.00 Depth: 9.00
Language: English
The next video in a feed is chosen from possibilities that the viewer never sees. That selection reflects what a platform knows, what it guesses, and what its designers have decided to reward. Recommendations can help people discover unfamiliar material while repeatedly favoring whatever has already attracted attention.

This book traces how a large collection becomes a short ranked list. Collaborative filtering, content features, candidate retrieval, and ranking objectives explain the basic machinery without requiring programming. Clicks and watch time receive particular scrutiny: they are convenient signals, but a recorded action does not reveal every reason behind it. New users, obscure items, and changing interests show why personalization is always working with incomplete information.

The discussion then follows recommendations back into the world, where exposure changes future behavior and future training data. Experiments, diversity measures, moderation rules, and user controls offer different ways to assess the resulting experience. Fairness is treated as a question about whose opportunities and preferences count, not a universal score. Readers gain a precise vocabulary for examining feeds, understanding tradeoffs, and questioning claims about what an algorithm knows about them.

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