Correlation-sensitive next-basket recommendation

• Main Authors: LE, Duc Trong, LAUW, Hady Wirawan, FANG, Yuan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Learning Preferences; Rankings; Recommender Systems; Databases and Information Systems; Operations Research, Systems Engineering and Industrial Engineering; Sales and Merchandising
• Online Access: https://ink.library.smu.edu.sg/sis_research/4434; https://ink.library.smu.edu.sg/context/sis_research/article/5437/viewcontent/main.pdf
• Institution: Singapore Management University

Items adopted by a user over time are indicative ofthe underlying preferences. We are concerned withlearning such preferences from observed sequencesof adoptions for recommendation. As multipleitems are commonly adopted concurrently, e.g., abasket of grocery items or a sitting of media consumption, we deal with a sequence of baskets asinput, and seek to recommend the next basket. Intuitively, a basket tends to contain groups of relateditems that support particular needs. Instead of recommending items independently for the next basket, we hypothesize that incorporating informationon pairwise correlations among items would help toarrive at more coherent basket recommendations.Towards this objective, we develop a hierarchicalnetwork architecture codenamed Beacon to modelbasket sequences. Each basket is encoded takinginto account the relative importance of items andcorrelations among item pairs. This encoding isutilized to infer sequential associations along thebasket sequence. Extensive experiments on threepublic real-life datasets showcase the effectivenessof our approach for the next-basket recommendation problem.