Session 1: Collaborative filtering 1

Date: Wednesday September 20, 11:15 AM – 12:35 PM (GMT+8)
Room: Hall 406CX
Session Chair: Toine Bogers
Parallel with: Session 2: Click-Through Rate prediction

  • RESAdversarial Collaborative Filtering for Free
    by Huiyuan Chen (Visa Research), Xiaoting Li (Visa Research), Vivian Lai (Visa Research), Chin-Chia Michael Yeh (Visa Research), Yujie Fan (Visa Research), Yan Zheng (Visa Research), Mahashweta Das (Visa Research) and Hao Yang (Visa Research).

    Collaborative Filtering (CF) has been successfully applied to help users discover the items of interest. Nevertheless, existing CF methods suffer from noisy data issue, which negatively impacts the quality of personalized recommendation. To tackle this problem, many prior studies leverage the adversarial learning principle to regularize the representations of users and items, which has shown great ability in improving both generalizability and robustness. Generally, those methods learn adversarial perturbations and model parameters using min-max optimization framework. However, there still have two major limitations: 1) Existing methods lack theoretical guarantees of why adding perturbations improve the model generalizability and robustness since noisy data is naturally different from adversarial attacks; 2) Solving min-max optimization is time-consuming. In addition to updating the model parameters, each iteration requires additional computations to update the perturbations, making them not scalable for industry-scale datasets.

    In this paper, we present Sharpness-aware Matrix Factorization (SharpMF), a simple yet effective method that conducts adversarial training without extra computational cost over the base optimizer. To achieve this goal, we first revisit the existing adversarial collaborative filtering and discuss its connection with recent Sharpness-aware Minimization. This analysis shows that adversarial training actually seeks model parameters that lie in neighborhoods having uniformly low loss values, resulting in better generalizability. To reduce the computational overhead, SharpMF introduces a novel trajectory loss to measure sharpness between current weights and past weights. Experimental results on real-world datasets demonstrate that our SharpMF achieves superior performance with almost zero additional computational cost comparing to adversarial training.

    Full text in ACM Digital Library

  • RESAugmented Negative Sampling for Collaborative Filtering
    by Yuhan Zhao (Harbin Engineering University), Rui Chen (Harbin Engineering University), Riwei Lai (Harbin Engineering University), Qilong Han (Harbin Engineering University), Hongtao Song (Harbin Engineering University) and Li Chen (Hong Kong Baptist University).

    Negative sampling is essential for implicit-feedback-based collaborative filtering, which is used to constitute negative signals from massive unlabeled data to guide supervised learning. The state-of-the-art idea is to utilize hard negative samples that carry more useful information to form a better decision boundary. To balance efficiency and effectiveness, the vast majority of existing methods follow the two-pass approach, in which the first pass samples a fixed number of unobserved items by a simple static distribution and then the second pass selects the final negative items using a more sophisticated negative sampling strategy. However, selecting negative samples from the original items from a dataset is inherently limited due to the limited available choices, and thus may not be able to contrast positive samples well. In this paper, we confirm this observation via carefully designed experiments and introduce two major limitations of existing solutions: ambiguous trap and information discrimination.

    Our response to such limitations is to introduce “augmented” negative samples that may not exist in the original dataset. This direction renders a substantial technical challenge because constructing unconstrained negative samples may introduce excessive noise that eventually distorts the decision boundary. To this end, we introduce a novel generic augmented negative sampling (ANS) paradigm and provide a concrete instantiation. First, we disentangle the hard and easy factors of negative items. Next, we generate new candidate negative samples by augmenting only the easy factors in a regulated manner: the direction and magnitude of the augmentation are carefully calibrated. Finally, we design an advanced negative sampling strategy to identify the final augmented negative samples, which considers not only the score used in existing methods but also a new metric called augmentation gain. Extensive experiments on five real-world datasets demonstrate that our method significantly outperforms state-of-the-art baselines. Our code is publicly available at

    Full text in ACM Digital Library

  • INDEfficient Data Representation Learning in Google-scale Systems
    by Derek Cheng (Google DeepMind), Ruoxi Wang (Google DeepMind), Wang-Cheng Kang (Google DeepMind), Benjamin Coleman (Google DeepMind), Yin Zhang (Google DeepMind), Jianmo Ni (Google DeepMind), Jonathan Valverde (Google DeepMind), Lichan Hong (Google DeepMind) and Ed Chi (Google DeepMind).

    Garbage in, Garbage out is a familiar maxim to ML practitioners and researchers, because the quality of a learned data representation is highly crucial to the quality of any ML model that consumes it as an input. To handle systems that serve billions of users at millions of queries per second (QPS), we need representation learning algorithms with significantly improved efficiency. At Google, we have dedicated thousands of iterations to develop a set of powerful techniques that efficiently learn high quality data representations.We have thoroughly validated these methods through offline evaluation, online A/B testing, and deployed these in over 50 models across major Google products. In this paper, we consider a generalized data representation learning problem that allows us to identify feature embeddings and crosses as common challenges. We propose two solutions, including: 1. Multi-size Unified Embedding to learn high-quality embeddings; and 2. Deep Cross Network V2 for learning effective feature crosses. We discuss the practical challenges we encountered and solutions we developed during deployment to production systems, compare with SOTA methods, and report offline and online experimental results. This work sheds light on the challenges and opportunities for developing next-gen algorithms for web-scale systems.

    Full text in ACM Digital Library

  • REPThe effect of third party implementations on reproducibility
    by Balázs Hidasi (Gravity R&D, a Taboola company) and Ádám Tibor Czapp (Gravity R&D, a Taboola company). The effect of third party implementations on reproducibility

    Reproducibility of recommender systems research has come under scrutiny during recent years. Along with works focusing on repeating experiments with certain algorithms, the research community has also started discussing various aspects of evaluation and how these affect reproducibility. We add a novel angle to this discussion by examining how unofficial third-party implementations could benefit or hinder reproducibility. Besides giving a general overview, we thoroughly examine six third-party implementations of a popular recommender algorithm and compare them to the official version on five public datasets. In the light of our alarming findings we aim to draw the attention of the research community to this neglected aspect of reproducibility.

    Full text in ACM Digital Library

Back to program

Diamond Supporter
Platinum Supporter
Amazon Science
Gold Supporter
Silver Supporter
Bronze Supporter
Challenge Sponsor
Special Supporters