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Predicting Redemption Probability of Gift Cards
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
TRITA-CSC-E, ISSN 1653-5715 ; 13:121
National Category
Computer Science
URN: urn:nbn:se:kth:diva-138296OAI: diva2:680745
Educational program
Master of Science in Engineering - Computer Science and Technology
Available from: 2013-12-18 Created: 2013-12-18 Last updated: 2015-08-28Bibliographically approved

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