• Corpus ID: 237592938

Context-aware Tree-based Deep Model for Recommender Systems

@article{Chang2021ContextawareTD,
  title={Context-aware Tree-based Deep Model for Recommender Systems},
  author={Daqing Chang and Jintao Liu and Ziru Xu and Han Li and Han Zhu and Xiaoqiang Zhu},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.10602}
}
How to predict precise user preference and how to make efficient retrieval from a big corpus are two major challenges of large-scale industrial recommender systems. In tree-basedmethods, a tree structure T is adopted as index and each item in corpus is attached to a leaf node on T . Then the recommendation problem is converted into a hierarchical retrieval problem solved by a beam search process efficiently. In this paper, we argue that the tree index used to support efficient retrieval in tree… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 41 REFERENCES
Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
TLDR
Experimental evaluations with two large-scale real-world datasets show that the proposed method improves recommendation accuracy significantly and a novel hierarchical user preference representation utilizing the tree index hierarchy is come up.
Learning Tree-based Deep Model for Recommender Systems
TLDR
A novel tree-based method which can provide logarithmic complexity w.r.t. corpus size even with more expressive models such as deep neural networks is proposed and can be jointly learnt towards better compatibility with users' interest distribution and hence facilitate both training and prediction.
SDM: Sequential Deep Matching Model for Online Large-scale Recommender System
TLDR
A new sequential deep matching (SDM) model to capture users' dynamic preferences by combining short-term sessions and long-term behaviors, which has been successfully deployed on online large-scale recommender system at Taobao and achieves improvements in terms of a range of commercial metrics.
Collaborative Deep Learning for Recommender Systems
TLDR
A hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix is proposed, which can significantly advance the state of the art.
Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks
TLDR
This paper introduces the concept of meta-graph to HIN-based recommendation, and solves the information fusion problem with a "matrix factorization + factorization machine (FM)" approach, and proposes to use FM with Group lasso (FMG) to automatically learn from the observed ratings to effectively select useful meta- graph based features.
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
TLDR
A novel method based on highly efficient random walks to structure the convolutions and a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model are developed.
Deep Matrix Factorization Models for Recommender Systems
TLDR
A novel matrix factorization model with neural network architecture is proposed that outperformed other state-of-the-art methods on several benchmark datasets and considers both explicit ratings and implicit feedback for a better optimization.
Session-based Recommendation with Graph Neural Networks
TLDR
In the proposed method, session sequences are modeled as graph-structured data and GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods.
Neural Graph Collaborative Filtering
TLDR
This work develops a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it, effectively injecting the collaborative signal into the embedding process in an explicit manner.
Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
TLDR
The technical solutions to address the scalability, sparsity and cold start problems of RS in Taobao are presented, and two aggregation methods to integrate the embeddings of items and the corresponding side information are proposed.
...
1
2
3
4
5
...