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I'm working on a recommendation engine for a simple application. The user is shown a photo which has a set of keywords; he or she can either like or dislike the current photo, and after 10 photos or so, the system should start providing recommendations.

My current plan is to maintain a term-frequency collection of all keywords corresponding to a like action. The cosine similarity will then be computed for each unseen image, and the top 10 results will be shown to the user as the next photos to like/dislike.

Are there more established techniques for this kind of thing?

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rookie
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1 Answers1

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Recommender systems are sitting on a wide range of methods to define similarity, handle sparsity, etc. Have a look at this.

user3639557
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