2009 GroceryShoppingRecommendationsb

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Grocery Shopping Recommendation, Basket Sensitive Random Walk, Popularity-based Performance Evaluation.

Abstract

We describe a recommender system in the domain of grocery shopping. While recommender systems have been widely studied, this is mostly in relation to leisure products (e.g. movies, books and music) with non-repeated purchases. In grocery shopping, however, consumers will make multiple purchases of the same or very similar products more frequently than buying entirely new items. The proposed recommendation scheme offers several advantages in addressing the grocery shopping problem, namely : 1) a product similarity measure that suits a domain where no rating information is available; 2) a basket sensitive random walk model to approximate product similarities by exploiting incomplete neighborhood information; 3) online adaptation of the recommendation based on the current basket and 4) a new performance measure focusing on products that customers have not purchased before or purchase infrequently. Empirical results benchmarking on three real-world data sets demonstrate a performance improvement of the proposed method over other existing collaborative filtering models.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2009 GroceryShoppingRecommendationsbMing Li
Benjamin M. Dias
Ian Jarman
Wael El-Deredy
Paulo J.G. Lisboa
Grocery Shopping Recommendations based on Basket-sensitive Random WalkKDD-2009 Proceedings10.1145/1557019.15571502009