Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item Prediction
ACM Transactions on Recommender Systems (TRS), 2024
- HAI
Main:33 Pages
20 Figures
Bibliography:2 Pages
9 Tables
Appendix:5 Pages
Abstract
Analyzing sequences of interactions between users and items, sequential recommendation models can learn user intent and make predictions about the next item. Next to item interactions, most systems also have interactions with what we call non-item pages: these pages are not related to specific items but still can provide insights of the user's interests, as, for example, navigation pages.
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