Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items
Benjamin Longxiang Wang, Sebastian Schelter

TL;DR
This paper introduces efficient algorithms for updating next basket recommendation models in response to additions and deletions of user baskets and items, ensuring privacy compliance and real-time responsiveness.
Contribution
It proposes novel incremental and decremental update algorithms for next basket recommendation models, enabling fast model adjustments under GDPR data deletion requirements.
Findings
Constant update time for incremental changes (~0.2 ms)
Linear update time for deletions (<1 ms)
Effective implementation in Spark Structured Streaming
Abstract
Recommender systems play an important role in helping people find information and make decisions in today's increasingly digitalized societies. However, the wide adoption of such machine learning applications also causes concerns in terms of data privacy. These concerns are addressed by the recent "General Data Protection Regulation" (GDPR) in Europe, which requires companies to delete personal user data upon request when users enforce their "right to be forgotten". Many researchers argue that this deletion obligation does not only apply to the data stored in primary data stores such as relational databases but also requires an update of machine learning models whose training set included the personal data to delete. We explore this direction in the context of a sequential recommendation task called Next Basket Recommendation (NBR), where the goal is to recommend a set of items based on…
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Taxonomy
TopicsRecommender Systems and Techniques · Privacy-Preserving Technologies in Data · Caching and Content Delivery
