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Item-side Ranking Regularized Distillation for Recommender System

1. Overview

This repository provides the source code of our paper: Item-side Ranking Regularized Distillation for Recommender System, accepted in Information Sciences'21.

In the paper, we propose Item-side ranking Regularization (IR) method for ranking distillation in Recommender System. The proposed IR method utilizes item-side ranking information, effectively preventing the student model from being overfitted and enabling the student model to more accurately learn the teacher’s prediction results.

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2. Evaluation

We evaluate the effectiveness of IR with the state-of-the-art ranking distillation method, RRD (CIKM'20).

2.1. Leave-One-Out (LOO) protocol

We provide the leave-one-out evaluation protocol used in the paper. The protocol is as follows:

2.2. Metrics

We provide three ranking metrics broadly adopted in the recent papers: HR@N, NDCG@N, MRR@N. The hit ratio simply measures whether the test item is present in the top-N list, which is defined as follows:

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where δ is the indicator function, U<sub>test</sub> is the set of the test users, p<sub>u</sub> is the hit ranking position of the test item for the user u. On the other hand, the normalized discounted cumulative gain and the mean reciprocal rank are ranking position-aware metrics that put higher scores to the hits at upper ranks. N@N and M@N are defined as follows:

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We also provide the training log and the learning curves of the proposed method. You can find them in /logs folder and the attached jupyter notebook.

Please note that the proposed IR method is for transferring knowledge from model's predictions. Topology Distillation (KDD'21), which is a follow-up study of DE and transfers the latent knowledge, is available in https://github.com/SeongKu-Kang/Topology_Distillation_KDD21