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Found 16 papers, 10 papers with code
Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs
It approximates the CE loss for datasets with large-size catalogs, enhancing both time efficiency and memory usage without compromising recommendations quality.
Ranked #1 on
Sequential Recommendation
on Amazon Beauty
RECE: Reduced Cross-Entropy Loss for Large-Catalogue Sequential Recommenders
Scalability is a major challenge in modern recommender systems.
End-to-End Graph-Sequential Representation Learning for Accurate Recommendations
Recent recommender system advancements have focused on developing sequence-based and graph-based approaches.
Dynamic Collaborative Filtering for Matrix- and Tensor-based Recommender Systems
In production applications of recommender systems, a continuous data flow is employed to update models in real-time.
Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction
Since the data about user experience are distributed among devices, the federated learning setup is used to train the proposed sequential matrix factorization model.
Mitigating Human and Computer Opinion Fraud via Contrastive Learning
We introduce the novel approach towards fake text reviews detection in collaborative filtering recommender systems.
Tensor-based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations
Self-attentive transformer models have recently been shown to solve the next item recommendation task very efficiently.
Tensor-based Collaborative Filtering With Smooth Ratings Scale
Conventional collaborative filtering techniques don't take into consideration the effect of discrepancy in users' rating perception.
Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks
The problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) which, due to its NP-hard complexity, is solved using Quantum Annealing on a quantum computer provided by D-Wave.
MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering
Knowledge Graphs (KGs) are symbolically structured storages of facts.
Dynamic Modeling of User Preferences for Stable Recommendations
In domains where users tend to develop long-term preferences that do not change too frequently, the stability of recommendations is an important factor of the perceived quality of a recommender system.
Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks
We introduce a simple autoencoder based on hyperbolic geometry for solving standard collaborative filtering problem.
Revealing the Unobserved by Linking Collaborative Behavior and Side Knowledge
We propose a tensor-based model that fuses a more granular representation of user preferences with the ability to take additional side information into account.
HybridSVD: When Collaborative Information is Not Enough
We propose a new hybrid algorithm that allows incorporating both user and item side information within the standard collaborative filtering technique.
Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks
In order to resolve this problem we propose to treat user feedback as a categorical variable and model it with users and items in a ternary way.
Tensor Methods and Recommender Systems
A substantial progress in development of new and efficient tensor factorization techniques has led to an extensive research of their applicability in recommender systems field.
