A content based recommender works with data that the user provides, either explicitly (rating) or implicitly (clicking on a link). Based on that data, items suggestion is generated. As the user provides more inputs or takes actions on the recommendations, the engine becomes more and more accurate.
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Proof of concept NN recommender built for ChapMarket. Works on the properties of the items user likes, discovering what else the user may like using content based filtering technique
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