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Semantic Web Recommender Systems [chapter]

Cai-Nicolas Ziegler
2004 Lecture Notes in Computer Science  
Future Directions Our past efforts have mainly focused on designing suitable trust metrics for computing trust neighborhoods [12] , and conceiving metrics for making collaborative filtering applicable  ...  Appleseed [12] , our own novel proposal for local group trust computation, allows more fine-grained analysis, assigning continuous trust ranks for peers within trust computation range.  ... 
doi:10.1007/978-3-540-30192-9_8 fatcat:mcgw6vw6jze5jcsden5xqu6e54

Integrating Social Circles and Network Representation Learning for Item Recommendation

Yonghong Yu, Qiang Wang, Li Zhang, Can Wang, Sifan Wu, Boyu Qi, Xiaotian Wu
2019 2019 International Joint Conference on Neural Networks (IJCNN)  
algorithms is limited by the coarse-grained trust relationships.  ...  Next, we adopt network representation technique to embed domain-specific social trust circle into a low-dimensional space, and then utilize the low-dimensional representations of users to infer the fine-grained  ...  circle. • Matrix factorization with the fine-grained trust values: for each domain, this component integrates the fine-grained trust relationships into classical matrix factorization model to learn domain-specific  ... 
doi:10.1109/ijcnn.2019.8852217 dblp:conf/ijcnn/YuW0WWQW19 fatcat:o2lrpuaw4vbo5kilka5ttfrai4

Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation

Jiagang Song, Jiayu Song, Xinpan Yuan, Xiao He, Xinghui Zhu
2022 Future Internet  
In addition, these collaborative filtering algorithms do not effectively consider the implicit trust relationship between users.  ...  One of the most widely used social network recommendation methods is collaborative filtering.  ...  This is especially to further integrate fine-grained user trust relationships based on the user-based collaborative filtering algorithm and to consider explicit and implicit trust between users to alleviate  ... 
doi:10.3390/fi14020032 fatcat:kfd5r7lwyvb7ndlh7dv35hvbdy

Using Trust of Social Ties for Recommendation

Liang CHEN, Chengcheng SHAO, Peidong ZHU, Haoyang ZHU
2016 IEICE transactions on information and systems  
. key words: social network, trust-based, collaborative filtering, random walk  ...  The motivation of our work is to deal with the above challenges by effectively combining collaborative filtering technology with social information.  ...  Different from TrustWalker, our approach uses the fine-grained trust value to form the trust network and performs the random walk based on the fine-grained trust value.  ... 
doi:10.1587/transinf.2015edp7199 fatcat:vq7aq2puevdnpnpsg2o6wrvhce

Exploration of Collaboration Filtering Techniques for Product Recommendation

2020 International Journal of Engineering and Advanced Technology  
In this paper, collaborative filtering task and their challenges are explored, study the different recommendation techniques and evaluate their performance using different metrics.  ...  Collaborative filtering is a proven techniques used in recommender system to make predictions or recommendations of the unknown preferences for users based on the known user preferences.  ...  In [10] , authors proposed an aspect sentiment collaborative filtering technique that obtained the users attributes towards aspect of the item through fined grained sentiment analysis on user's transaction  ... 
doi:10.35940/ijeat.c5348.029320 fatcat:vg74s2gnbzdmlj3r5czt7teb4e

Network Representation Learning Enhanced Recommendation Algorithm

Qiang Wang, Yonghong Yu, Haiyan Gao, Li Zhang, Yang Cao, Lin Mao, Kaiqi Dou, Wenye Ni
2019 IEEE Access  
Finally, we integrate the fine-grained and dense trust relationships into the matrix factorization model to learn user and item latent feature vectors.  ...  Socialnetwork-based recommendation algorithms generally assume that users with trust relations usually share common interests.  ...  , matrix factorization with the fine-grained trust values, and rating prediction.  ... 
doi:10.1109/access.2019.2916186 fatcat:vngczvs4czg4jgrbocsmbarjke

Reviewer Credibility and Sentiment Analysis based User Profile Modelling for Online Product Recommendation

Shigang Hu, Akshi Kumar, Fadi Al-Turjman, Shivam Gupta, Simran Seth, Shubham
2020 IEEE Access  
This research work augments heuristic-driven user interest profiling with reviewer credibility analysis and fine-grained feature sentiment analysis to devise a robust recommendation methodology.  ...  To make our model robust to fake and unworthy reviews and reviewers, reviewer credibility analysis proffers an approach of associating expertise, trust and influence scores with reviewers to weigh their  ...  collaborative filtering (gain = 24%).  ... 
doi:10.1109/access.2020.2971087 fatcat:23mozn64vrbfdffq6cbtiglkeq

Frontiers in Trust Management

Christian Damsgaard Jensen, Nicola Dragoni, Anirban Basu, Clara Mancini
2011 Journal of Internet Services and Information Security  
paper of Basu, Vaidya and Kikuchi [2], entitled "Efficient Privacy-Preserving Collaborative Filtering Based on the Weighted Slope One Predictor" examines the privacy of users in collaborative filtering  ...  Lu and Tsudik propose a new approach to cloud storage, based on attribute-based encryption and the blind Boneh-Boyen weak signature scheme to ensure data privacy and fine-grained access control. • The  ...  metrics derived from social networks.  ... 
doi:10.22667/jisis.2011.11.31.001 dblp:journals/jisis/JensenDBM11 fatcat:4xhkqsamure2thexkcwmqarnd4

Similarity-based Techniques for Trust Management [chapter]

Mozhgan Tavakolifard
2010 Web Intelligence and Intelligent Agents  
of blog articles, thus enabling trust relationships to be evaluated in a fine-grained manner.  ...  Profile Level trust is a less fine-grained metric, representing a recommendation www.intechopen.com producers trust as a whole, without respect to one specific item.  ...  This book presents a unique and diversified collection of research work ranging from controlling the activities in virtual world to optimization of productivity in games, from collaborative recommendations  ... 
doi:10.5772/8386 fatcat:aqgkxzkwqfa33jwtncjyr2gaoq

Paradigms for Decentralized Social Filtering Exploiting Trust Network Structure [chapter]

Cai-Nicolas Ziegler, Georg Lausen
2004 Lecture Notes in Computer Science  
Moreover, we present an implementation of one suchlike trust-based recommender and perform empirical analysis to underpin its fitness when coupled with an intelligent, content-based filter.  ...  We advocate trust metrics and trust-driven neighborhood formation as an appropriate surrogate, and outline various additional benefits of harnessing trust networks for recommendation generation purposes  ...  Appleseed [35] , our own proposal for local group trust computation, allows more fine-grained analysis, assigning continuous trust weights for peers within trust computation range.  ... 
doi:10.1007/978-3-540-30469-2_2 fatcat:m7hf24zuababrchxkz6sn5lvhm

Is trust robust?

John O'Donovan, Barry Smyth
2006 Proceedings of the 11th international conference on Intelligent user interfaces - IUI '06  
In this paper we examine the effect of using five different trust models in the recommendation process on the robustness of collaborative filtering in an attack situation.  ...  Recent research has shown that incorporating trust and reputation models into the recommendation process can have a positive impact on the accuracy of recommendations.  ...  5), and a more fine grained item-level trust, which is the mean trust score for over every recommendation p has generated for a given item i.  ... 
doi:10.1145/1111449.1111476 dblp:conf/iui/ODonovanS06 fatcat:xe5y6bcg4bgspdjnvdt6gbh2oy

Trust in recommender systems

John O'Donovan, Barry Smyth
2005 Proceedings of the 10th international conference on Intelligent user interfaces - IUI '05  
We present two computational models of trust and show how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways.  ...  And collaborative filtering techniques have proven to be an vital component of many such recommender systems as they facilitate the generation of high-quality recommendations by leveraging the preferences  ...  Accordingly we can define a more fine-grained item-level trust metric, T rust I , as shown in Equation 3 , which measures the percentage of recommendations for an item i that were correct.  ... 
doi:10.1145/1040830.1040870 dblp:conf/iui/ODonovanS05 fatcat:etxkucqx5zdytpfb2ok5v6gmgy

MINING TRUST VALUES FROM RECOMMENDATION ERRORS

JOHN O'DONOVAN, BARRY SMYTH
2006 International journal on artificial intelligence tools  
We show how this trust-based technique can be easily incorporated into a standard collaborative filtering algorithm and define a fair comparison in which our technique outperforms a benchmark algorithm  ...  Collaborative filtering is a successful and popular technique for producing recommendations based on similarities in users' tastes and opinions.  ...  Profile Level trust is a less fine-grained metric, representing a recommendation producers trust as a whole, without respect to one specific item.  ... 
doi:10.1142/s0218213006003053 fatcat:xtsmo7gjbfdcrljb7pabarrwka

Fine-Grained Recommendation Systems for Service Attribute Exchange [chapter]

Christopher Staite, Rami Bahsoon, Stephen Wolak
2009 Lecture Notes in Computer Science  
Unlike current recommendation systems which provide a user with a general trust value for a service, we propose a reputation model which calculates trust neighbourhoods through fine-grained multi-attribute  ...  Such a model allows a recommendation relevance to improve whilst maintaining a large user group, propagating and evolving trust perceptions between users.  ...  Existing recommendation systems take coarse-grained approaches to analysis of trust by means of a single metric.  ... 
doi:10.1007/978-3-642-10383-4_24 fatcat:cekone3rqzga5me5tpog5qdaua

A Social Recommendation Based on Metric Learning and Users' Co-Occurrence Pattern

Xin Zhang, Jiwei Qin, Jiong Zheng
2021 Symmetry  
Metric learning-based models have been shown to be better at capturing fine-grained information than matrix factorization.  ...  For personalized recommender systems, matrix factorization and its variants have become mainstream in collaborative filtering.  ...  However, the dot product in matrix factorization does not satisfy the triangle inequality and therefore fails to capture fine-grained information.  ... 
doi:10.3390/sym13112158 fatcat:zaik6hmkkneubnzfhapcfuhnaa
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