The paper shows that latent topics can be extracted directly from the weight matrix of a closed‑form linear autoencoder under a latent‑factor model of user‑item interactions. These topics enable interpretation of recommendations without extra training, and the authors introduce item and user normalizations to mitigate dominance by popular items or active users. Experiments on six public datasets demonstrate that the method outperforms dedicated topic models in topic quality and matches or exceeds the best linear autoencoder, while maintaining competitive recommendation performance.
Linear recommenders and topic models are similar in that they implicitly learn from co-occurrences of items. However, there has been little study of how these two model families are related. In this w...
arXiv:2611.06610v1 Announce Type: new Abstract: Collaborative filtering (CF) is a cornerstone of recommender systems and plays a relevant role in many modern applications. CF uses user-item interacti...
The paper investigates why many state‑of‑the‑art recommendation algorithms, despite using diverse deep‑learning techniques, achieve similar performance. It shows that the key commonality is a regularizer: either a nuclear‑norm or a Frobenius‑norm term. The authors further propose two new low‑rank, closed‑form solutions that combine the advantages of both regularizers.
This reproducibility study investigates the counterfactual explanation method Learning to eXplain Recommendations (LXR) by extending its application beyond the top‑ranked item to other recommended items. The authors compare LXR against alternative baselines such as item popularity and latent factor similarity, noting that these baselines perform surprisingly well on certain tasks. They also analyze how item popularity affects explanation quality, finding that LXR produces shorter perturbations for less popular items, and conduct a brute‑force search to estimate a lower bound on optimal perturbation size.
arXiv:2403. 00802v2 Announce Type: replace-cross Abstract: Production-grade recommender systems rely heavily on a large-scale corpus used by online media services, including Netflix, Pinterest, and Amazon.
Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a result, these systems often suffer from the black-box nature and computational complexity of the underlying models, making it difficult to systematically enhance their recommendation capabilities.
arXiv:2606. 01948v1 Announce Type: cross Abstract: The growing popularity of group activities has increased the need for methods that provide recommendations to groups of users given their individual preferences.
arXiv:2607. 20863v1 Announce Type: cross Abstract: Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items.
arXiv:2606. 16973v1 Announce Type: cross Abstract: Incorporating textual reviews into a Recommender System has become a prominent strategy for enriching collaborative signals with semantic information.
arXiv:2606. 18897v1 Announce Type: cross Abstract: Intent-based recommender systems have gained significant attention for improving accuracy and interpretability by modeling the underlying motivations behind user behaviors.
The paper introduces CLARER, a recommendation model that fuses aspect features extracted from textual reviews with rating data to enhance recommendation accuracy and explainability. It learns user and item representations by combining rating-based features via an MLP and aspect-based features via a transformer encoder with contrastive learning. A transformer decoder then generates explanations using the combined representations, and experiments on three benchmark datasets show superior performance over baseline methods in both recommendation accuracy and explanation generation.
arXiv:2610.35550v1 Announce Type: cross Abstract: Cross-domain sequential recommendation aims to improve next-item prediction in a target domain by leveraging user behavior from auxiliary source doma...