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I want to build a recommender system for a coupons website which should do the following: Given the past purchase behaviour of a user, recommend coupons which the user is likely to buy. The data does not have any ratings for coupons by the user. It tells if a user bought a certain coupon or not and what is the gender and location of the user who bought these coupons. Between content-based and collborative-filtering what would be the best recommender system to choose from in this scenario. Personally, I think that content based recommender system fits the requirement because I want to look only at user's own historical behaviour. What would you guys advice? Thanks.

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I think it will strongly depend on the details of the content you have for the coupons, but you can always train an hybrid model with different options of selection for the final recommendation, like weighting the results or selecting the top one of each model. The most important thing is that you build a reliable test set and can compare performances between models.

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You are right. Content-Based Recommender System is the best approach to tackle such problems using historic information about the users.

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