Can keywords help personalized recommendation for coupon deals?

In this paper we focus on personalized recommendation algorithm for coupon deals, which are very different from deals of other retailers. We first analyzed some sample deals from Groupon and found that deals under category dining, Wellness and activities have a high probability of having the same keywords in the deal names, which may suggest a repeated buying behavior. We believe we can use keyword matching technique for recommendation for these categories. To prove this hypothesis, we conduct experiments on a private dataset from another company doing business similar to Groupon, and our findings from the experiments show that most of the deals from those categories bought by people have keywords matched to previous deals bought by the same user. Finally, we propose a new recommendation algorithm which conducts keyword matching on the preliminary results from traditional collaborative filtering methods, and evaluation on this algorithm will be the further research direction.