121,153 research outputs found
Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss
Contrastive Learning (CL) has achieved impressive performance in
self-supervised learning tasks, showing superior generalization ability.
Inspired by the success, adopting CL into collaborative filtering (CF) is
prevailing in semi-supervised top-K recommendations. The basic idea is to
routinely conduct heuristic-based data augmentation and apply contrastive
losses (e.g., InfoNCE) on the augmented views. Yet, some CF-tailored challenges
make this adoption suboptimal, such as the issue of out-of-distribution, the
risk of false negatives, and the nature of top-K evaluation. They necessitate
the CL-based CF scheme to focus more on mining hard negatives and
distinguishing false negatives from the vast unlabeled user-item interactions,
for informative contrast signals. Worse still, there is limited understanding
of contrastive loss in CF methods, especially w.r.t. its generalization
ability. To bridge the gap, we delve into the reasons underpinning the success
of contrastive loss in CF, and propose a principled Adversarial InfoNCE loss
(AdvInfoNCE), which is a variant of InfoNCE, specially tailored for CF methods.
AdvInfoNCE adaptively explores and assigns hardness to each negative instance
in an adversarial fashion and further utilizes a fine-grained hardness-aware
ranking criterion to empower the recommender's generalization ability. Training
CF models with AdvInfoNCE, we validate the effectiveness of AdvInfoNCE on both
synthetic and real-world benchmark datasets, thus showing its generalization
ability to mitigate out-of-distribution problems. Given the theoretical
guarantees and empirical superiority of AdvInfoNCE over most contrastive loss
functions, we advocate its adoption as a standard loss in recommender systems,
particularly for the out-of-distribution tasks. Codes are available at
https://github.com/LehengTHU/AdvInfoNCE.Comment: Accepted to NeurIPS 202
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Policy-Aware Unbiased Learning to Rank for Top-k Rankings
Counterfactual Learning to Rank (LTR) methods optimize ranking systems using
logged user interactions that contain interaction biases. Existing methods are
only unbiased if users are presented with all relevant items in every ranking.
There is currently no existing counterfactual unbiased LTR method for top-k
rankings. We introduce a novel policy-aware counterfactual estimator for LTR
metrics that can account for the effect of a stochastic logging policy. We
prove that the policy-aware estimator is unbiased if every relevant item has a
non-zero probability to appear in the top-k ranking. Our experimental results
show that the performance of our estimator is not affected by the size of k:
for any k, the policy-aware estimator reaches the same retrieval performance
while learning from top-k feedback as when learning from feedback on the full
ranking. Lastly, we introduce novel extensions of traditional LTR methods to
perform counterfactual LTR and to optimize top-k metrics. Together, our
contributions introduce the first policy-aware unbiased LTR approach that
learns from top-k feedback and optimizes top-k metrics. As a result,
counterfactual LTR is now applicable to the very prevalent top-k ranking
setting in search and recommendation.Comment: SIGIR 2020 full conference pape
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