scholarly journals Social Recommendation Model with User Trust and Item Ratings Using Collaborative Filtering Technique in Hotel Application

2020 ◽  
Vol 01 (01) ◽  
pp. 17-22
Author(s):  
Mary Harin Fernandez F ◽  
◽  
Ramya S ◽  
Revathy V ◽  
◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
State Of The Art ◽  
Security And Privacy ◽  
Social Recommendation ◽  
Friend Recommendation ◽  
Trust Networks ◽  
Filtering Technique ◽  
Privacy Issues ◽  
User Trust ◽  
Confidence Data

A trust-based recommendation model is regularized with user trust and item ratings called TrustSVD. Trust networks are large-world networks where many users are socially linked, suggesting the assumption of trust in recommendation systems. An item rating downloaded from the OSN Server can be viewed by the user. If the information is accessible on the server, all the adjacent devices are enabled and a peer to peer mode of communication is initiated. User reviews from a graphical forum are shown. It focuses on the rating prediction role in the current framework and has shown that integrating user social confidence data will boost the output of recommendations. The strategy builds on the SVD++ state-of-the-art model. The data sparsity and cold start issues are resolved in the friend of friend recommendation model used. The mining method generates the user's overall rating in graphical representations and illustrates the overall rating. This model increases the utility of data by exchanging neighborhoods to protect security and privacy issues. One of the most common techniques for implementing a recommendation scheme is Collaborative filtering (CF).

scholarly journals A Comprehensive Survey on Security and Privacy for Electronic Health Data

2021 ◽  
Vol 18 (18) ◽  
pp. 9668
Author(s):  
Se-Ra Oh ◽  
Young-Duk Seo ◽  
Euijong Lee ◽  
Young-Gab Kim
Keyword(s):  
Health Systems ◽  
State Of The Art ◽  
Fog Computing ◽  
Health Data ◽  
Security And Privacy ◽  
Health Security ◽  
Comprehensive Survey ◽  
Security Concerns ◽  
Edge And Fog Computing ◽  
Privacy Issues

Recently, the integration of state-of-the-art technologies, such as modern sensors, networks, and cloud computing, has revolutionized the conventional healthcare system. However, security concerns have increasingly been emerging due to the integration of technologies. Therefore, the security and privacy issues associated with e-health data must be properly explored. In this paper, to investigate the security and privacy of e-health systems, we identified major components of the modern e-health systems (i.e., e-health data, medical devices, medical networks and edge/fog/cloud). Then, we reviewed recent security and privacy studies that focus on each component of the e-health systems. Based on the review, we obtained research taxonomy, security concerns, requirements, solutions, research trends, and open challenges for the components with strengths and weaknesses of the analyzed studies. In particular, edge and fog computing studies for e-health security and privacy were reviewed since the studies had mostly not been analyzed in other survey papers.

scholarly journals On the Security and Privacy Challenges of Virtual Assistants

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2312
Author(s):  
Tom Bolton ◽  
Tooska Dargahi ◽  
Sana Belguith ◽  
Mabrook S. Al-Rakhami ◽  
Ali Hassan Sodhro
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Security And Privacy ◽  
Future Research ◽  
Research Directions ◽  
Privacy Concerns ◽  
Current Trends ◽  
Current State ◽  
Future Research Directions ◽  
Privacy Issues

Since the purchase of Siri by Apple, and its release with the iPhone 4S in 2011, virtual assistants (VAs) have grown in number and popularity. The sophisticated natural language processing and speech recognition employed by VAs enables users to interact with them conversationally, almost as they would with another human. To service user voice requests, VAs transmit large amounts of data to their vendors; these data are processed and stored in the Cloud. The potential data security and privacy issues involved in this process provided the motivation to examine the current state of the art in VA research. In this study, we identify peer-reviewed literature that focuses on security and privacy concerns surrounding these assistants, including current trends in addressing how voice assistants are vulnerable to malicious attacks and worries that the VA is recording without the user’s knowledge or consent. The findings show that not only are these worries manifold, but there is a gap in the current state of the art, and no current literature reviews on the topic exist. This review sheds light on future research directions, such as providing solutions to perform voice authentication without an external device, and the compliance of VAs with privacy regulations.

scholarly journals Efficient Graph Collaborative Filtering via Contrastive Learning

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4666
Author(s):  
Zhiqiang Pan ◽  
Honghui Chen
Keyword(s):  
Collaborative Filtering ◽  
State Of The Art ◽  
Representation Learning ◽  
First Order ◽  
Satisfactory Performance ◽  
Potential Applications ◽  
Benchmark Datasets ◽  
Bayesian Personalized Ranking ◽  
Graph Neural Networks ◽  
Training Efficiency

Collaborative filtering (CF) aims to make recommendations for users by detecting user’s preference from the historical user–item interactions. Existing graph neural networks (GNN) based methods achieve satisfactory performance by exploiting the high-order connectivity between users and items, however they suffer from the poor training efficiency problem and easily introduce bias for information propagation. Moreover, the widely applied Bayesian personalized ranking (BPR) loss is insufficient to provide supervision signals for training due to the extremely sparse observed interactions. To deal with the above issues, we propose the Efficient Graph Collaborative Filtering (EGCF) method. Specifically, EGCF adopts merely one-layer graph convolution to model the collaborative signal for users and items from the first-order neighbors in the user–item interactions. Moreover, we introduce contrastive learning to enhance the representation learning of users and items by deriving the self-supervisions, which is jointly trained with the supervised learning. Extensive experiments are conducted on two benchmark datasets, i.e., Yelp2018 and Amazon-book, and the experimental results demonstrate that EGCF can achieve the state-of-the-art performance in terms of Recall and normalized discounted cumulative gain (NDCG), especially on ranking the target items at right positions. In addition, EGCF shows obvious advantages in the training efficiency compared with the competitive baselines, making it practicable for potential applications.

scholarly journals A social trust and preference segmentation-based matrix factorization recommendation algorithm

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Wei Peng ◽  
Baogui Xin
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
User Preference ◽  
Social Recommendation ◽  
Recommendation Algorithm ◽  
Commercial Activities ◽  
Trust Relationships ◽  
Recommendation Algorithms ◽  
The Difference

AbstractA recommendation can inspire potential demands of users and make e-commerce platforms more intelligent and is essential for e-commerce enterprises’ sustainable development. The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas. To solve these problems mentioned above, we propose a social trust and preference segmentation-based matrix factorization (SPMF) recommendation algorithm. Experimental results based on the Ciao and Epinions datasets show that the accuracy of the SPMF algorithm is significantly superior to that of some state-of-the-art recommendation algorithms. The SPMF algorithm is a better recommendation algorithm based on distinguishing the difference of trust relations and preference domain, which can support commercial activities such as product marketing.

User opinions driven social recommendation system

2021 ◽  
Vol 25 (1) ◽  
pp. 21-31
Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Social Recommendation ◽  
Novel Approach ◽  
Tripartite Graphs ◽  
Content Based Filtering

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

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