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I implemented all the code with Python and a fork of my current framework Crab, a python framework for building recommender systems and variations.Īll the algorithm explained can be accessed at this link.Omni Group OmniFocus 2 Pro discount Omni Group OmniFocus 2 Pro discount Apple Final Cut Pro X Omni Group OmniPlan 3 Pro Pitney Bowes MapInfo Professional 15.2. Of course several external factors could influence this result, but showed us promising results at our first attempt. When you buy through links on RetailMeNot we may earn a commission. In 4 months running we achieved an increase of almot 300% in click-through-rates per user thant without any ranking methods. But it was our baseline and at our tests in production was very satisfactory. It's simple and of course there are many improvements that could be made. We also came with this approach but using different features exclusive at our domain.įrank(u,d) = w1 * SD + w2 * ED + w3*P + w4*CA + w5*UP + w6*DV + b I liked the NetFlix approach on his recommendation problem at their company, where they came up with a simple scoring approach by choosing the ranking function to be a linear combination of several features. In the end, in order to produce rankings that balance all of these aspects we came wih a prediction model using all those features. In Favoritoz people could follow retailers (their favorites) and categories (sports, cuisine, automobile, etc.) We use also this information in the ranking equation. Another important feature is the user taste. Furthermore, let's look into the number of coupons available, in this case, a deal that are almost with no coupons left, could atract more users to purchase the last ones. So let's use another information like the publishing date and expiration date of the deal which could give us the newest deals and the deals that are almost close to end. But it can lead to deals that are common to everyone and it's the opposite of personalization. For instance, we look for "hot" deals, which is represented by most bought deals (popularity).
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But the question is how to order those deals in a way that users could interest at or even convert into transactions ? That's my work! I came with a simple ranking algorithm that could use various type of information available in order to come with a useful ranking system that could recommend the best deals for each of our customers at Favoritoz.īy looking to specific features in our domain (coupons market) we could use them to build a ranking function that could give some personalized recommendations.
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Generaly those items are organized in some form of list, such as, in our scenario, the various deals organized as wallboard on Favoritoz.
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This task is accomplished by sorting some itens in ther order of utility or interest for the person. 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.