A compositional model of multi-faceted trust for personalized item recommendation

Ardissono, Liliana; Mauro, Noemi

doi:10.1016/j.eswa.2019.112880

Cited by 25 publications

(17 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lee et al [17] proved that users' evaluations are affected by other users and that users connected by a network of trust exhibit significantly higher similarity on items. Ardissono et al [18] proposed a compositional recommender system based on multi-faceted trust using social links and global feedback about users. Jaehoon et al [19] proposed a new trust recommendation algorithm, TCRec.…”

Section: Related Workmentioning

confidence: 99%

A genre trust model for defending shilling attacks in recommender systems

Niu

2021

Complex Intell. Syst.

View full text Add to dashboard Cite

Shilling attacks have been a significant vulnerability of collaborative filtering (CF) recommender systems, and trust in CF recommender algorithms has been proven to be helpful for improving the accuracy of system recommendations. As a few studies have been devoted to trust in this area, we explore the benefits of using trust to resist shilling attacks. Rather than simply using user-generated trust values, we propose the genre trust degree, which differ in terms of the genres of items and take both trust value and user credibility into consideration. This paper introduces different types of shilling attack methods in an attempt to study the impact of users’ trust values and behavior features on defending against shilling attacks. Meanwhile, it improves the approach used to calculate user similarities to form a recommendation model based on genre trust degrees. The performance of the genre trust-based recommender system is evaluated on the Ciao dataset. Experimental results demonstrated the superior and comparable genre trust degrees recommended for defending against different types of shilling attacks.

show abstract

Section: Related Workmentioning

confidence: 99%

A genre trust model for defending shilling attacks in recommender systems

Niu

2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…Liu et al [10] proposed a model that takes into account the local contextual information of user ratings and the global preferences of user behaviors.This model solved the problem of inaccurate trust evaluation caused by low user ratings, and can improve recommendation performance.In order to protect the privacy of users, and to consider as much evidence as possible, using limited information to predict the trust value of users is now required to be resolved. Liliana Ardissono et al's [11]trust-based recommendation system adds evidence of trust on the basis of publicly anonymous information, that is, personal contribution quality and multi-dimensional global reputation. Xin Wang et al [12] combined neural network methods and Dempster-Shafer theory to study trust and distrust.…”

Section: Related Workmentioning

confidence: 99%

Trust Prediction Based on Extreme Learning Machine and Asymmetric Tri-Training

Wang

Tong

2021

IEEE Access

View full text Add to dashboard Cite

Currently, more and more people communicate and trade through online social networks, so it is necessary to predict the trust between users. In order to predict the trust between users through a social network requires some other factors, such as user similarity, topic relevance, and the number of common neighbors should be considered. Some scholars use BP neural network based on the asymmetric tri-training model to train one social network and use transfer learning to predict the social relationship of another social network. However, this method has high computational complexity and slow operation speed. To solve the problem, the three classifiers are expanded into four classifiers on the basis of asymmetric tritraining, and the extreme learning machine classifier is used to replace the BP neural network classifier. The specific steps are: we first obtain the common features between different networks, use source data samples to train the first three classifiers, save the model, and train target samples to generate pseudo labels; then we use standard pseudo label samples to train the fourth classifier and use it to predict the social relations of the target network. Finally, compare with the existing evaluation methods,we proposed an algorithm on six online social networks. The experimental results show that the model in this paper is superior to other evaluation methods in recall and stability.INDEX TERMS asymmetric tri-training; extreme learning machine; trust prediction; transfer learning; pseudo label

show abstract

“…They noticed that those trustors who follow the same trustee, having similar tastes and shared common features that improve the performance of socialbased recommendations [103]. Ardissono proposed a LOCABAL+ model that is a trust-based recommendation model by defining a multifaceted trust by users feedback and generated top N recommendations [108]. Noh et al developed a trust aware model and studied the impact of power users available in social networks and exploited the trust cluster and observed that clusters with normal user provided better results [104].…”

Section: Ctrust-based Recommendation Systemsmentioning

confidence: 99%

Modeling Trust-Aware Recommendations With Temporal Dynamics in Social Networks

et al. 2020

View full text Add to dashboard Cite

In recent years, user's trust has gained attention in recommender systems. Trust plays a vital role in the recommendation of online products. Trust is a dynamic feature which evolves with passage of time and varies from person to person. Trust-based cross domain recommender systems suggest items to the users usually by ratings, provided by similar users, often not available in the same domain. However, due to the sparse rating scores, recommender systems cannot generate up-to-the-mark recommendations. In this research, we solved a user cold start problem, mainly by modeling preference drift on a temporal basis. We tried to solve this problem by adopting one of the scenarios of cross domain of 'No Overlap' using cross domain information. In this work, we proposed a model called Trust Aware Cross Domain Temporal Recommendations (TrustCTR) that predict the rating of an item about an active user from the most recent time. We generated user features and item features by using latent factor model and trained the proposed model. We also introduced the concept of trust relevancy that shows the degree of trust, computed the trusted neighbors in target domain for an active user belonging to a source domain, and predicted the ratings of items for cold start users. We performed experiments on public datasets Ciao and Epinions and used these datasets in cross domain form such as the categories of Ciao as source domain and Epinions as the target domain. We selected five different domains, having a higher proportion of rating sparsity, for observing the performance of our approach using MAE, RMSE, and F-measure. Our approach is a viable solution of cold start problem and offers effective recommendations We also compared the model with state-of-the-art methods; the model generates satisfactory results.

show abstract

A compositional model of multi-faceted trust for personalized item recommendation

Cited by 25 publications

References 65 publications

A genre trust model for defending shilling attacks in recommender systems

A genre trust model for defending shilling attacks in recommender systems

Trust Prediction Based on Extreme Learning Machine and Asymmetric Tri-Training

Modeling Trust-Aware Recommendations With Temporal Dynamics in Social Networks

Contact Info

Product

Resources

About