UNLEARNING THE UNWANTED DATA FROM A PERSONALIZED RECOMMENDATION MODEL
Primary Area: general machine learning (i.e., none of the above)
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Keywords: machine unlearning, recommendation system, matrix factorization
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Abstract: Recommender Systems (RS) learn user behavior by monitoring their activities on the online platform. In a few scenarios, users consume the content but don’t want to get their recommendations because a). They consumed the content by mistake, and those interactions have been utilized in personalizing the model; b) The content was consumed by someone else on their behalf; c) Data acquisition was faulty because of machine failure; d) The user has lost interest in the service, etc. Out of any of these reasons, the user wants the data that was used for generating the recommendation to be unlearned by RS. The constraints with this unlearning are 1) The user’s other data should be intact, 2) Personalized experience should not be affected, and 3) We can not afford training from scratch. To solve the stated problem, a few unlearning strategies have already been proposed, but unlearning the matrix factorization-based model is not much explored. In this work, we propose a solution of unlearning from the faulty recommendation model (m1) by diluting the impact of unwanted data. To do so, we first correct the unwanted data and pre- pare an intermediate tiny model m2, referred to as the rescue model. Further, we apply the convolution fusion function (CFF) on the latent features acquired using m1 , m2 . The performance of the proposed method is evaluated on multiple public datasets. We observed that the proposed method outperforms SOTA benchmark models on recommendation tasks.
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Submission Number: 3195
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