114 research outputs found
Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets
The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS source code is available along with a representative set of generated datasets. We have tested the GANRS method by creating multiple synthetic datasets from three different real datasets taken as a source. Experiments include variations in the number of users in the synthetic datasets, as well as a different number of samples. We have also selected six state-of-the-art collaborative filtering deep learning models to test both their comparative performance and the GANRS method. The results show a consistent behavior of the generated datasets compared to the source ones; particularly, in the obtained values and trends of the precision and recall quality measures. The tested deep learning models have also performed as expected on all synthetic datasets, making it possible to compare the results with those obtained from the real source data. Future work is proposed, including different cold start scenarios, unbalanced data, and demographic fairness
Multi-Modal Self-Supervised Learning for Recommendation
The online emergence of multi-modal sharing platforms (eg, TikTok, Youtube)
is powering personalized recommender systems to incorporate various modalities
(eg, visual, textual and acoustic) into the latent user representations. While
existing works on multi-modal recommendation exploit multimedia content
features in enhancing item embeddings, their model representation capability is
limited by heavy label reliance and weak robustness on sparse user behavior
data. Inspired by the recent progress of self-supervised learning in
alleviating label scarcity issue, we explore deriving self-supervision signals
with effectively learning of modality-aware user preference and cross-modal
dependencies. To this end, we propose a new Multi-Modal Self-Supervised
Learning (MMSSL) method which tackles two key challenges. Specifically, to
characterize the inter-dependency between the user-item collaborative view and
item multi-modal semantic view, we design a modality-aware interactive
structure learning paradigm via adversarial perturbations for data
augmentation. In addition, to capture the effects that user's modality-aware
interaction pattern would interweave with each other, a cross-modal contrastive
learning approach is introduced to jointly preserve the inter-modal semantic
commonality and user preference diversity. Experiments on real-world datasets
verify the superiority of our method in offering great potential for multimedia
recommendation over various state-of-the-art baselines. The implementation is
released at: https://github.com/HKUDS/MMSSL.Comment: This paper has been published as a full paper at WWW 202
Editable User Profiles for Controllable Text Recommendation
Methods for making high-quality recommendations often rely on learning latent
representations from interaction data. These methods, while performant, do not
provide ready mechanisms for users to control the recommendation they receive.
Our work tackles this problem by proposing LACE, a novel concept value
bottleneck model for controllable text recommendations. LACE represents each
user with a succinct set of human-readable concepts through retrieval given
user-interacted documents and learns personalized representations of the
concepts based on user documents. This concept based user profile is then
leveraged to make recommendations. The design of our model affords control over
the recommendations through a number of intuitive interactions with a
transparent user profile. We first establish the quality of recommendations
obtained from LACE in an offline evaluation on three recommendation tasks
spanning six datasets in warm-start, cold-start, and zero-shot setups. Next, we
validate the controllability of LACE under simulated user interactions.
Finally, we implement LACE in an interactive controllable recommender system
and conduct a user study to demonstrate that users are able to improve the
quality of recommendations they receive through interactions with an editable
user profile.Comment: Accepted to SIGIR 2023; Pre-print, camera-ready to follo
Robust Representation Learning for Unified Online Top-K Recommendation
In large-scale industrial e-commerce, the efficiency of an online
recommendation system is crucial in delivering highly relevant item/content
advertising that caters to diverse business scenarios. However, most existing
studies focus solely on item advertising, neglecting the significance of
content advertising. This oversight results in inconsistencies within the
multi-entity structure and unfair retrieval. Furthermore, the challenge of
retrieving top-k advertisements from multi-entity advertisements across
different domains adds to the complexity. Recent research proves that
user-entity behaviors within different domains exhibit characteristics of
differentiation and homogeneity. Therefore, the multi-domain matching models
typically rely on the hybrid-experts framework with domain-invariant and
domain-specific representations. Unfortunately, most approaches primarily focus
on optimizing the combination mode of different experts, failing to address the
inherent difficulty in optimizing the expert modules themselves. The existence
of redundant information across different domains introduces interference and
competition among experts, while the distinct learning objectives of each
domain lead to varying optimization challenges among experts. To tackle these
issues, we propose robust representation learning for the unified online top-k
recommendation. Our approach constructs unified modeling in entity space to
ensure data fairness. The robust representation learning employs domain
adversarial learning and multi-view wasserstein distribution learning to learn
robust representations. Moreover, the proposed method balances conflicting
objectives through the homoscedastic uncertainty weights and orthogonality
constraints. Various experiments validate the effectiveness and rationality of
our proposed method, which has been successfully deployed online to serve real
business scenarios.Comment: 14 pages, 6 figures, submitted to ICD
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