Recommender systems aim to present desirable items for a person to choose from. All those aspects could be measured in order to compute a overall score that would be used to rank those recommendable items. My goal was to develop this baseline for ranking deals based on their interests, but not only using the user profile, also we could use particular aspects from the on-line deals such as: the initial date of the deal, the popularity of the deal or the number of coupons left for that deal. For instance, If l liked sushis, videogames and junk-food, the system could detect those interests and using our algorithm it would rank higher offers and deals related by those topics.
One of the critical features was the personalization, where people could say wha they would like to receive daily at their deals wall. Favoritoz was on-line coupon system where retailers could publish offers with special discounts or their products to segmented customers interested at their products or by a specific brand.
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