Predicting Redemption Probability of Gift Cards Combining Rating and Demographic Data in a Hybrid User Based Recommender System

Recommender systems try to facilitate the decision making process of users, by recommending products such as movies, music and news articles. This work uses a user based recommender system to predict redemption probabilities of different gift cards. That is, the probability that a user redeems a gift card in a store, given that he or she receives it. This work is a base for ranking gift cards in the future. Two collaborative filtering algorithms are evaluated, both based on neighbour recommender methods. The data are provided by the digital gift giving company Wrapp. The nearest neighbours are chosen by similarity, based on the rating of gift cards and the demographic data of the users. The result shows that it is possible to predict redemption probabilities of gift cards with this data. It also shows that it is important to include certain user behaviors when predicting the redemption probabilities. One such example is if a user tends to redeem more or less gift cards than other users. This work does not explicitly show that demographic data are improving the result, compared to a rating data approach, even though the results with demographic data seem promising.