Journal Article10.1007/s10489-022-03452-0
Item sequential recommendation based on graph embedding model
Chenkun Zhang,Cheng Wang +1 more
1
TL;DR: Experimental results on the Jdata, HetRec2011, and MIND-small datasets show that SAEGES is superior to DEEPWALK, Node2vec, and EGES, in respect of AUC, andSAEGES-SSE-PT is also superior to the self-attention-based sequential model (SASRec) and SSE- PT inrespect of Normalized Discounted Cumulative Gain, recall, and execution time.
read more
About: This article is published in Applied Intelligence. The article was published on 18 Mar 2022. The article focuses on the topics: Computer science & Computer science.
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
Relevance-aware graph neural network for session-based recommendation
Ya Zeng,Bo Yang,Dong-Chi Li +2 more
TL;DR: A model named Relevance-Aware Graph Neural Network (RA-GNN) is proposed in this paper, which captures and utilizes both types of information carried in a session, i.e., both the information of item-to-item transitions and the know-nothing information, which could help improve the performance of session-based recommendation.
References
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
DeepWalk: online learning of social representations
Bryan Perozzi,Rami Al-Rfou,Steven Skiena +2 more
- 24 Aug 2014
TL;DR: DeepWalk as mentioned in this paper uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences, which encode social relations in a continuous vector space, which is easily exploited by statistical models.
node2vec: Scalable Feature Learning for Networks
Aditya Grover,Jure Leskovec +1 more
- 13 Aug 2016
TL;DR: Node2vec as mentioned in this paper learns a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes by using a biased random walk procedure.
•Posted Content
node2vec: Scalable Feature Learning for Networks
Aditya Grover,Jure Leskovec +1 more
TL;DR: In node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks, a flexible notion of a node's network neighborhood is defined and a biased random walk procedure is designed, which efficiently explores diverse neighborhoods.
6.6K
LINE: Large-scale Information Network Embedding
Jian Tang,Meng Qu,Mingzhe Wang,Ming Zhang,Jun Yan,Qiaozhu Mei +5 more
- 18 May 2015
TL;DR: A novel network embedding method called the ``LINE,'' which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted, and optimizes a carefully designed objective function that preserves both the local and global network structures.