Proceedings Article10.1109/ICDE.2019.00140
Neural Multi-task Recommendation from Multi-behavior Data
Chen Gao,Xiangnan He,Dahua Gan,Xiangning Chen,Fuli Feng,Yong Li,Tat-Seng Chua,Depeng Jin +7 more
- 08 Apr 2019
- pp 1554-1557
186
TL;DR: In this paper, the authors proposed Neural Multi-Task Recommendation (NMTR) for learning recommender systems from user multi-behavior data, which accounts for the cascading relationship among different types of behaviors and performs a joint optimization based on the multi-task learning framework.
read more
Abstract: Most existing recommender systems leverage user behavior data of one type, such as the purchase behavior data in E-commerce. We argue that other types of user behavior data also provide valuable signal, such as views, clicks, and so on. In this work, we contribute a new solution named NMTR (short for Neural Multi-Task Recommendation) for learning recommender systems from user multi-behavior data. In particular, our model accounts for the cascading relationship among different types of behaviors (e.g., a user must click on a product before purchasing it). We perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task. Extensive experiments on the real-world dataset demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Efficient Neural Matrix Factorization without Sampling for Recommendation
TL;DR: This work derives three new optimization methods through rigorous mathematical reasoning, which can efficiently learn model parameters from the whole data with a rather low time complexity, and presents a general framework named ENMF, short for Efficient Neural Matrix Factorization.
207
Denoising Implicit Feedback for Recommendation
TL;DR: This work proposes a new training strategy named Adaptive Denoising Training (ADT), which adaptively prunes noisy interactions during training and demonstrates that ADT significantly improves the quality of recommendation over normal training.
202
Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation
Chong Chen,Min Zhang,Yongfeng Zhang,Weizhi Ma,Yiqun Liu,Shaoping Ma +5 more
- 03 Apr 2020
TL;DR: This work proposes a novel non-sampling transfer learning solution, named Efficient Heterogeneous Collaborative Filtering (EHCF) for Top-N recommendation that can not only model fine-grained user-item relations, but also efficiently learn model parameters from the whole heterogeneous data with a rather low time complexity.
Reinforced negative sampling for recommendation with exposure data
Jingtao Ding,Yuhan Quan,Xiangnan He,Yong Li,Depeng Jin +4 more
- 10 Aug 2019
TL;DR: This work designs a novel RNS method (short for Reinforced Negative Sampler) that generates exposure-alike negative instances through feature matching technique instead of directly choosing from exposure data, and is able to integrate user preference signals in exposure data and hard negatives.
Contrastive Meta Learning with Behavior Multiplicity for Recommendation
Chao Huang,Liang Xia,Yong Xu,Jiashu Zhao,Dawei Yin +4 more
- 11 Feb 2022
TL;DR: A multi-behavior contrastive learning framework to distill transferable knowledge across different types of behaviors via the constructed contrastive loss, and a contrastive meta network to encode the customized behavior heterogeneity for different users are proposed.
References
Neural Collaborative Filtering
Xiangnan He,Lizi Liao,Hanwang Zhang,Liqiang Nie,Xia Hu,Tat-Seng Chua +5 more
- 03 Apr 2017
TL;DR: This work strives to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback, and presents a general framework named NCF, short for Neural network-based Collaborative Filtering.
•Proceedings Article
BPR: Bayesian personalized ranking from implicit feedback
Steffen Rendle,Christoph Freudenthaler,Zeno Gantner,Lars Schmidt-Thieme +3 more
- 18 Jun 2009
TL;DR: In this article, the authors proposed a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem, which is based on stochastic gradient descent with bootstrap sampling.
Regularized multi--task learning
Theodoros Evgeniou,Massimiliano Pontil +1 more
- 22 Aug 2004
TL;DR: An approach to multi--task learning based on the minimization of regularization functionals similar to existing ones, such as the one for Support Vector Machines, that have been successfully used in the past for single-- task learning is presented.
Relational learning via collective matrix factorization
Ajit P. Singh,Geoffrey J. Gordon +1 more
- 24 Aug 2008
TL;DR: This model generalizes several existing matrix factorization methods, and therefore yields new large-scale optimization algorithms for these problems, which can handle any pairwise relational schema and a wide variety of error models.
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
Xiangnan He,Hanwang Zhang,Min-Yen Kan,Tat-Seng Chua +3 more
- 07 Jul 2016
TL;DR: A new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique is designed, for efficiently optimizing a Matrix Factorization (MF) model with variably-weighted missing data and exploiting this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback.
Related Papers (5)
Xiangnan He,Lizi Liao,Hanwang Zhang,Liqiang Nie,Xia Hu,Tat-Seng Chua +5 more
- 03 Apr 2017
Xiang Wang,Xiangnan He,Meng Wang,Fuli Feng,Tat-Seng Chua +4 more
- 18 Jul 2019