Recency Aware Collaborative Filtering for Next Basket Recommendation
Guglielmo Faggioli, Mirko Polato, Fabio Aiolli
Abstract
E-commerce and online services are getting more and more ubiquitous day by day. Like many other e-commerce paradigms, online grocery services can highly benefit from recommender systems, especially when it comes to predicting users' shopping behavior. This specific scenario owns peculiar characteristics, such as repetitiveness and loyalty, which makes the task very different from the standard recommendations. In this work, we present an efficient solution to compute the next basket recommendation, under a more general top-n recommendation framework. We propose a set of collaborative filtering based techniques able to capture users' shopping patterns. Furthermore, we analyzed how recency plays a key role in this particular task. We finally compare our method with state-of-the-art algorithms on two online grocery service datasets.