TY - JOUR

T1 - PromotionRank

T2 - Ranking and Recommending Grocery Product Promotions Using Personal Shopping Lists

AU - Nurmi, Petteri

AU - Salovaara, Antti

AU - Forsblom, Andreas

AU - Bohnert, Fabian

AU - Floréen, Patrik

PY - 2014/4/1

Y1 - 2014/4/1

N2 - We present PromotionRank, a technique for generating a personalized ranking of grocery product promotions based on the contents of the customer’s personal shopping list. PromotionRank consists of four phases. First, information retrieval techniques are used to map shopping list items onto potentially relevant product categories. Second, since customers typically buy more items than what appear on their shopping lists, the set of potentially relevant categories is expanded using collaborative filtering. Third, we calculate a rank score for each category using a statistical interest criterion. Finally, the available promotions are ranked using the newly computed rank scores. To validate the different phases, we consider 12 months of anonymized shopping basket data from a large national supermarket. To demonstrate the effectiveness of PromotionRank, we also present results from two user studies. The first user study was conducted in a controlled setting using shopping lists of different lengths, whereas the second study was conducted within a large national supermarket using real customers and their personal shopping lists. The results of the two studies demonstrate that PromotionRank is able to identify promotions that are considered both relevant and interesting. As part of the second study, we used PromotionRank to identify relevant promotions to advertise and measure the influence of the advertisements on purchases. The results of this evaluation indicate that PromotionRank is also capable of targeting advertisements, improving sales compared to a baseline that selects random advertisements.

AB - We present PromotionRank, a technique for generating a personalized ranking of grocery product promotions based on the contents of the customer’s personal shopping list. PromotionRank consists of four phases. First, information retrieval techniques are used to map shopping list items onto potentially relevant product categories. Second, since customers typically buy more items than what appear on their shopping lists, the set of potentially relevant categories is expanded using collaborative filtering. Third, we calculate a rank score for each category using a statistical interest criterion. Finally, the available promotions are ranked using the newly computed rank scores. To validate the different phases, we consider 12 months of anonymized shopping basket data from a large national supermarket. To demonstrate the effectiveness of PromotionRank, we also present results from two user studies. The first user study was conducted in a controlled setting using shopping lists of different lengths, whereas the second study was conducted within a large national supermarket using real customers and their personal shopping lists. The results of the two studies demonstrate that PromotionRank is able to identify promotions that are considered both relevant and interesting. As part of the second study, we used PromotionRank to identify relevant promotions to advertise and measure the influence of the advertisements on purchases. The results of this evaluation indicate that PromotionRank is also capable of targeting advertisements, improving sales compared to a baseline that selects random advertisements.

KW - Ranking, advertising, personalization, recommender Systems, retailing, user study

U2 - 10.1145/2584249

DO - 10.1145/2584249

M3 - Journal article

VL - 4

SP - 1:1-1:23

JO - ACM Transactions on Interactive Intelligent Systems (TiiS)

JF - ACM Transactions on Interactive Intelligent Systems (TiiS)

SN - 2160-6455

IS - 1

ER -