检索结果-南通市图书馆

Self-supervised graph learning with target-adaptive masking for session-based recommendation

Frontiers of Information Technology & Electronic Engineering 2023年第1期24卷 73-87页

作者： Yitong WANG Fei CAI Zhiqiang PAN Chengyu SONG Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410073China

session-based recommendation aims to predict the next item based on a user’s limited interactions within a short *** approaches use mainly recurrent neural networks(RNNs)or graph neural networks(GNNs)to model the sequential patterns or the transition relationships between ***,such models either ignore the over-smoothing issue of GNNs,or directly use cross-entropy loss with a softmax layer for model optimization,which easily results in the over-fitting *** tackle the above issues,we propose a self-supervised graph learning with target-adaptive masking(SGL-TM)***,we first construct a global graph based on all involved sessions and subsequently capture the self-supervised signals from the global connections between items,which helps supervise the model in generating accurate representations of items in the ongoing *** that,we calculate the main supervised loss by comparing the ground truth with the predicted scores of items adjusted by our designed target-adaptive masking ***,we combine the main supervised component with the auxiliary self-supervision module to obtain the final loss for optimizing the model *** experimental results from two benchmark datasets,Gowalla and Diginetica,indicate that SGL-TM can outperform state-of-the-art baselines in terms of Recall@20 and MRR@20,especially in short sessions.

关键词： session-based recommendation Self-supervised learning Graph neural networks Target-adaptive

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation

引用

Frontiers of Computer Science 2023年第5期17卷 103-118页

作者： Jinwei LUO Mingkai HE Weike PAN Zhong MING College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen 518060China

session-based recommendation(SBR)and multibehavior recommendation(MBR)are both important problems and have attracted the attention of many researchers and *** from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics,heterogeneous SBR(HSBR)that exploits different types of behavioral information(e.g.,examinations like clicks or browses,purchases,adds-to-carts and adds-to-favorites)in sequences is more consistent with real-world recommendation scenarios,but it is rarely *** efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of ***,all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of ***,all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of *** limitation hinders the development of HSBR and results in unsatisfactory *** a response,we propose a novel behavior-aware graph neural network(BGNN)for *** BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a ***,our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified *** then conduct extensive empirical studies on three real-world datasets,and find that our BGNN outperforms the best baseline by 21.87%,18.49%,and 37.16%on average correspondingly.A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our *** exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can e

关键词： session-based recommendation graph neural network heterogeneous behaviors

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

BA-GNN: Behavior-aware graph neural network for session-based recommendation

引用

Frontiers of Computer Science 2023年第6期17卷 135-144页

作者： Yongquan LIANG Qiuyu SONG Zhongying ZHAO Hui ZHOU Maoguo GONG School of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China School of Electronic Engineering Xidian UniversityXi’an 710071China

session-based recommendation is a popular research topic that aims to predict users’next possible interactive item by exploiting anonymous *** existing studies mainly focus on making predictions by considering users’single interactive *** recent efforts have been made to exploit multiple interactive behaviors,but they generally ignore the influences of different interactive behaviors and the noise in interactive *** address these problems,we propose a behavior-aware graph neural network for session-based ***,different interactive sequences are modeled as directed ***,the item representations are learned via graph neural ***,a sparse self-attention module is designed to remove the noise in behavior ***,the representations of different behavior sequences are aggregated with the gating mechanism to obtain the session *** results on two public datasets show that our proposed method outperforms all competitive *** source code is available at the website of GitHub.

关键词： session-based recommendation multiple interactive behaviors graph neural networks

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Modeling Price-Aware session-based recommendation based on Graph Neural Network

引用

Computers, Materials & Continua 2023年第7期76卷 397-413页

作者： Jian Feng Yuwen Wang Shaojian Chen College of Computer Science&Technology Xi’an University of Science and TechnologyXi’an710600China

session-based recommendation(SBR)aims to accurately recom-mend a list of items to users based on anonymous historical session *** methods for SBR suffer from several limitations:SBR based on Graph Neural Network often has information loss when constructing session graphs;Inadequate consideration is given to influencing factors,such as item price,and users’dynamic interest evolution is not taken into account.A new session recommendation model called Price-aware session-based recommendation(PASBR)is proposed to address these *** constructs session graphs by information lossless approaches to fully encode the original session information,then introduces item price as a new factor and models users’price tolerance for various items to influence users’*** addition,PASBR proposes a new method to encode user intent at the item category level and tries to capture the dynamic interest of users over ***,PASBR fuses the multi-perspective features to generate the global representation of users and make a ***,the intent,the short-term and long-term interests,and the dynamic interests of a user are *** on two real-world datasets show that PASBR can outperform representative baselines for SBR.

关键词： session-based recommendation graph neural network price-aware intention dynamic interest

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

SGT:session-based recommendation with GRU and Transformer

引用

Journal of Computer Science Research 2023年第2期5卷 37-51页

作者： Lingmei Wu Liqiang Zhang Xing Zhang Linli Jiang Chunmei Wu School of Mathematics&Computer Science Guangxi Science&Technology Normal UniversityLaibinGuangxi546199China

session-based recommendation aims to predict user preferences based on anonymous behavior *** research on session-based recommendation systems has mainly focused on utilizing attention mechanisms on sequential patterns,which has achieved significant ***,most existing studies only consider individual items in a session and do not extract information from continuous items,which can easily lead to the loss of information on item transition ***,this paper proposes a session-based recommendation algorithm(SGT)based on Gated Recurrent Unit(GRU)and Transformer,which captures user interests by learning continuous items in the current session and utilizes all item transitions on sessions in a more refined *** combining short-term sessions and long-term behavior,user dynamic preferences are *** experiments were conducted on three session-based recommendation datasets,and compared to the baseline methods,both the recall rate Recall@20 and the mean reciprocal rank MRR@20 of the SGT algorithm were improved,demonstrating the effectiveness of the SGT method.

关键词： Recommender system Gated recurrent unit Transformer session-based recommendation Graph neural networks

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intention-aware for session-based recommendation with Multi-channel Network

引用

Journal of Literature and Art Studies 2021年第3期11卷 196-204页

作者： WANG Jing-jing Oliver Tat Sheung Au Lap-Kei Lee School of Science and Technology The Open University of Hong KongHong KongChina

session-based recommendation predicts the user’s next action by exploring the item dependencies in an anonymous *** of the existing methods are based on the assumption that each session has a single intention,items irrelevant to the single intention will be regarded as ***,in real-life scenarios,sessions often contain multiple *** paper designs a multi-channel Intention-aware Recurrent Unit(TARU)network to further mining these *** multi-channel TARU explicitly group items into the different channels by filtering items irrelevant to the current intention with the intention control ***,we propose to use the attention mechanism to adaptively generate an effective representation of the session’s final preference for the *** experimental results on two real-world datasets denote that our method performs well in session recommendation tasks and achieves improvement against several baselines on the general metrics.

关键词： Intention-aware network session-based recommendation recommendation

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

基于多关系感知和自监督学习的超图增强会话推荐模型

引用

建模与仿真 2024年第4期13卷 4104-4119页

作者：王益飞翟雨欣上海理工大学光电信息与计算机工程学院，上海

会话推荐旨在基于匿名用户的历史行为序列预测下一个最有可能交互的项目。现有的大多数基于超图解决会话推荐模型没有考虑超边同构，忽略了会话内可能存在的多种依赖关系，从而无法捕获会话的潜在信息。同时，会话匿名性和短序列往往会... 详细信息

会话推荐旨在基于匿名用户的历史行为序列预测下一个最有可能交互的项目。现有的大多数基于超图解决会话推荐模型没有考虑超边同构，忽略了会话内可能存在的多种依赖关系，从而无法捕获会话的潜在信息。同时，会话匿名性和短序列往往会导致数据稀疏性，影响了推荐模型的性能。因此本文提出一种基于多关系感知和自监督学习的超图增强会话推荐模型(MS-HGNN)，该模型能够有效捕获会话间用户的复杂高阶关系和会话内顺序及共现依赖关系。具体来说，MS-HGNN模型将会话序列分别建模为超图、共现图和顺序图，在捕获超图的同时加入位置信息以减轻超边同构，利用门控机制融合会话内顺序依赖关系和共现依赖关系。此外，使用自监督学习最大化两个会话表示之间的互信息以缓解数据稀疏性，有效地融合用户的复杂高阶关系和会话内顺序及共现依赖关系。广泛的实验结果表明，所提出的MS-HGNN模型在三个真实数据集Diginetica、Tmall和***上的性能优于目前最先进的模型。

关键词：会话推荐超图神经网络多关系感知自监督学习 session-based recommendation Hypergraph Neural Network Multi-Relationship Awareness Self-Supervised Learning

来源：博看期刊评论

在线全文

博看期刊

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：