Publication Type
Journal Article
Version
acceptedVersion
Publication Date
9-2025
Abstract
We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility to learn a set of associations and combinations between different interaction types in a way that carries over to each user and item. Second, our model is able to learn jointly from both the explicit ratings and the implicit information in the sampling pattern (which we refer to as ”implicit feedback”). It can also make separate predictions for the probability of consuming content and the likelihood of granting it a high rating if observed. This not only allows the model to make predictions for both the implicit and explicit feedback, but also increases the informativeness of the predictions: in particular, our model can identify items that users would not have been likely to consume naturally, but would be likely to enjoy if exposed to them. Finally, we provide several generalization bounds for our model, which, to the best of our knowledge, are among the first generalization bounds for autoencoders in a Recommender systems setting; we also show that optimizing our loss function guarantees the recovery of the exact sampling distribution over interactions up to a small error in total variation. In experiments on several real-life datasets, we achieve state-of-the-art performance on both the implicit and explicit feedback prediction tasks despite relying on a single model for both, and benefiting from additional interpretability in the form of individual predictions for the probabilities of each possible rating.
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
IEEE Transactions on Neural Networks and Learning Systems
Volume
36
Issue
12
First Page
20035
Last Page
20049
ISSN
2162-237X
Identifier
10.1109/TNNLS.2025.3597051
Publisher
Institute of Electrical and Electronics Engineers
Citation
LEDENT, Antoine; KASALICKÝ, Petr; ALVES, Rodrigo; and LAUW, Hady Wirawan.
Conv4Rec: A 1‑by‑1 convolutional autoencoder for user profiling through joint analysis of implicit and explicit feedbacks. (2025). IEEE Transactions on Neural Networks and Learning Systems. 36, (12), 20035-20049.
Available at: https://ink.library.smu.edu.sg/sis_research/10834
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TNNLS.2025.3597051