Tensor-based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations

• URL: http://arxiv.org/abs/2212.05720v1
• Date: Mon, 12 Dec 2022 05:55:40 GMT
• Title: Tensor-based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations
• Authors: Evgeny Frolov and Ivan Oseledets
• Abstract summary: Self-attentive transformer models have been shown to solve the next item recommendation task very efficiently. Motivated by the special structure of learned parameter space, we question if it is possible to mimic it with an alternative and more lightweight approach. We develop a new tensor factorization-based model that ingrains the structural knowledge about sequential data within the learning process.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Self-attentive transformer models have recently been shown to solve the next item recommendation task very efficiently. The learned attention weights capture sequential dynamics in user behavior and generalize well. Motivated by the special structure of learned parameter space, we question if it is possible to mimic it with an alternative and more lightweight approach. We develop a new tensor factorization-based model that ingrains the structural knowledge about sequential data within the learning process. We demonstrate how certain properties of a self-attention network can be reproduced with our approach based on special Hankel matrix representation. The resulting model has a shallow linear architecture and compares competitively to its neural counterpart.

Related papers

• Group and Shuffle: Efficient Structured Orthogonal Parametrization [3.540195249269228]
  We introduce a new class of structured matrices, which unifies and generalizes structured classes from previous works. We empirically validate our method on different domains, including adapting of text-to-image diffusion models and downstream task fine-tuning in language modeling.
  arXiv  Detail & Related papers  (2024-06-14T13:29:36Z)
• Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks [0.0]
  We show that precious information is contained in the spectrum of the precision matrix that can be extracted once the training of the model is completed. We conducted numerical experiments for regression, classification, and feature selection tasks. Our results demonstrate that the proposed model does not only yield an attractive prediction performance compared to the competitors.
  arXiv  Detail & Related papers  (2023-07-11T09:54:30Z)
• Robust Graph Representation Learning via Predictive Coding [46.22695915912123]
  Predictive coding is a message-passing framework initially developed to model information processing in the brain. In this work, we build models that rely on the message-passing rule of predictive coding. We show that the proposed models are comparable to standard ones in terms of performance in both inductive and transductive tasks.
  arXiv  Detail & Related papers  (2022-12-09T03:58:22Z)
• A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms [64.3064050603721]
  We generalize Runge-Kutta neural network to a recurrent neural network (R2N2) superstructure for the design of customized iterative algorithms. We demonstrate that regular training of the weight parameters inside the proposed superstructure on input/output data of various computational problem classes yields similar iterations to Krylov solvers for linear equation systems, Newton-Krylov solvers for nonlinear equation systems, and Runge-Kutta solvers for ordinary differential equations.
  arXiv  Detail & Related papers  (2022-11-22T16:30:33Z)
• Neural Eigenfunctions Are Structured Representation Learners [93.53445940137618]
  This paper introduces a structured, adaptive-length deep representation called Neural Eigenmap. We show that, when the eigenfunction is derived from positive relations in a data augmentation setup, applying NeuralEF results in an objective function. We demonstrate using such representations as adaptive-length codes in image retrieval systems.
  arXiv  Detail & Related papers  (2022-10-23T07:17:55Z)
• Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation [57.67616822888859]
  We propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL) TMER-RL utilizes reinforcement item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendation. Extensive evaluations of TMER on two real-world datasets show state-of-the-art performance compared against recent strong baselines.
  arXiv  Detail & Related papers  (2021-11-24T04:34:26Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Structured Reordering for Modeling Latent Alignments in Sequence Transduction [86.94309120789396]
  We present an efficient dynamic programming algorithm performing exact marginal inference of separable permutations. The resulting seq2seq model exhibits better systematic generalization than standard models on synthetic problems and NLP tasks.
  arXiv  Detail & Related papers  (2021-06-06T21:53:54Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• Causality-aware counterfactual confounding adjustment for feature representations learned by deep models [14.554818659491644]
  Causal modeling has been recognized as a potential solution to many challenging problems in machine learning (ML) We describe how a recently proposed counterfactual approach can still be used to deconfound the feature representations learned by deep neural network (DNN) models.
  arXiv  Detail & Related papers  (2020-04-20T17:37:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.