检索结果-南通市图书馆

Leveraging implicit social structures for recommendation via a Bayesian generative model

Science China(Information Sciences) 2022年第4期65卷 269-271页

作者： Huafeng LIU Jingxuan WEN Liping JING Jian YU School of Computer and Information Technology Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University

Dear editor,social recommendation leverages the social relations between users and their past behaviors to model user preferences. While social recommendation is a core component in recommendation systems, discovering... 详细信息

关键词： recommendation system Collaborative filtering social recommendation social networks Generative model

来源：

同方期刊数据库

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

social Trust Aware Item recommendation for Implicit Feedback

引用

Journal of Computer Science & Technology 2015年第5期30卷 1039-1053页

作者：郭磊马军姜浩然陈竹敏刑长明 School of Management Science and Engineering Shandong Normal University Jinan 250014 China School of Computer Science and Technology Shandong University Jinan 250101 China Bureau of Information Technology Shandong Post Company Jinan 250101 China School of Continuing Education Shandong University of Finance and Economics Jinan 250101 China

social trust aware recommender systems have been well studied in recent years. However, most of existing methods focus on the recommendation scenarios where users can provide explicit feedback to items. But in most cases, the feedback is not explicit but implicit. Moreover, most of trust aware methods assume the trust relationships among users are single and homogeneous, whereas trust as a social concept is intrinsically multi-faceted and heterogeneous. Simply exploiting the raw values of trust relations cannot get satisfactory results. Based on the above observations, we propose to learn a trust aware personalized ranking method with multi-faceted trust relations for implicit feedback. Specifically, we first introduce the social trust assumption -- a user＇s taste is close to the neighbors he/she trusts into the Bayesian Personalized Ranking model. To explore the impact of users＇ multi-faceted trust relations, we further propose a category- sensitive random walk method CRWR to infer the true trust value on each trust link. Finally, we arrive at our trust strength aware item recommendation method socialBPRcawn by replacing the raw binary trust matrix with the derived real-valued trust strength. Data analysis and experimental results on two real-world datasets demonstrate the existence of social trust influence and the effectiveness of our social based ranking method socialBPRcawR in terms of AUC （area under the receiver operating characteristic curve）.

关键词： social recommendation matrix factorization random walk Bayesian personalized ranking

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

Exploiting Pre-Trained Network Embeddings for recommendations in social Networks

引用

Journal of Computer Science & Technology 2018年第4期33卷 682-696页

作者： Lei Guo Yu-Fei Wen Xin-Hua Wang Postdoctoral Research Station of Management Science and Engineering Shandong Normal University Jinan 250014 China School of Management Science and Engineering Shandong Normal University Jinan 250014 China School of Information Science and Engineering Shandong Normal University Jinan 250014 China

Recommender systems as one of the most efficient information filtering techniques have been widely studied in recent years. However, traditional recommender systems only utilize user-item rating matrix for recommendations, and the social connections and item sequential patterns are ignored. But in our real life, we always turn to our friends for recommendations, and often select the items that have similar sequential patterns. In order to overcome these challenges, many studies have taken social connections and sequential information into account to enhance recommender systems. Although these existing studies have achieved good results, most of them regard social influence and sequential information as regularization terms, and the deep structure hidden in social networks and rating patterns has not been fully explored. On the other hand, neural network based embedding methods have shown their power in many recommendation tasks with their ability to extract high-level representations from raw data. Motivated by the above observations, we take the advantage of network embedding techniques and propose an embedding-based recommendation method, which is composed of the embedding model and the collaborative filtering model. Specifically, to exploit the deep structure hidden in social networks and rating patterns, a neural network based embedding model is first pre-trained, where the external user and item representations are extracted. Then, we incorporate these extracted factors into a collaborative filtering model by fusing them with latent factors linearly, where our method not only can leverage the external information to enhance recommendation, but also can exploit the advantage of collaborative filtering techniques. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed method and the importance of these external extracted factors.

关键词： social recommendation network embedding matrix factorization item sequential pattern

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

Two-sided regularization model based on probabilistic matrix factorization and quantum similarity for recommender systems

引用

International Journal of Modeling, Simulation, and Scientific Computing 2020年第6期11卷 129-160页

作者： Waleed Reafee Marwa Alhazmi Naomie Salim Faculty of Computer Studies and Information Technology University of Garden City 11111 KhartoumSudan School of Computing Faculty of Engineering Universiti Teknologi Malaysia81310 SkudaiJohorMalaysia

Nowadays,with the advent of the age of Web 2.0,several social recommendation methods that use social network information have been proposed and achieved distinct develop***,the most critical challenges for the existing majority of these methods are:(1)They tend to utilize only the available social relation between users and deal just with the cold-start user issue.(2)Besides,these methods are suffering from the lack of exploitation of content information such as social tagging,which can provide various sources to extract the item information to overcome the cold-start item and improve the recommendation *** this paper,we investigated the efficiency of data fusion by integrating multi-source of ***,two essential factors,user-side information,and item-side information,are ***,we developed a novel social recommendation model called Two-Sided Regularization(TSR),which is based on the probabilistic matrix factorization ***,the effective quantum-based similarity method is adapted to measure the similarity between users and between items into the proposed *** results on the real dataset show that our proposed model TSR addresses both of cold-start user and item issues and outperforms state-ofthe-art recommendation *** results indicate the importance of incorporating various sources of information in the recommendation process.

关键词： social recommendation explicit friendship implicit friendship correlated items quantum mechanics

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

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

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

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：