Correlation-sensitive next-basket recommendation
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,...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2019
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| Subjects: | |
| 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 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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