scholarly journals Research on Collaborative Filtering Recommendation Based on Trust Relationship and Rating Trust

2021 ◽  
Vol 1 (2) ◽  
pp. 1-9
Author(s):  
Wenjun Huang ◽  
Junyu Chen ◽  
Yue Ding
Keyword(s):  
Information Overload ◽  
Social Trust ◽  
Matrix Decomposition ◽  
Trust Relationship ◽  
Internet Age ◽  
Trust Relationships ◽  
Recommendation Algorithms ◽  
Implicit Trust ◽  
Relationship Of ◽  
User Trust

In the Internet age, how to dig out useful information from massive data has become a research hotspot. The emergence of recommendation algorithms effectively solves the problem of information overload, but traditional recommendation algorithms face problems such as data sparseness, cold start, and low accuracy. Later social recommendation algorithms usually only use a single social trust information for recommendation, and the integration of multiple trust relationships lacks an efficient model, which greatly affects the accuracy and reliability of recommendation. This paper proposes a trust-based approach. Recommended algorithm. First, use social trust data to calculate user trust relationships, including user local trust and user global trust. Further based on the scoring data, an implicit trust relationship is calculated, called rating trust, which includes scoring local trust and scoring global trust. Then set the recommendation weight, build the preference relationship between users through user trust and rating trust, and form a comprehensive trust relationship. The trust relationship of social networks is integrated into the probability matrix decomposition model to form an efficient and unified trusted recommendation model TR-PMF. This algorithm is compared with related algorithms on the Ciao and FilmTrust datasets, and the results prove that our method is competitive with other recommendation algorithms.

A mobile services recommendation system fuses implicit and explicit user trust relationships

2021 ◽  
Vol 13 (1) ◽  
pp. 21-35
Author(s):  
Pengcheng Luo ◽  
Jilin Zhang ◽  
Jian Wan ◽  
Nailiang Zhao ◽  
Zujie Ren ◽  
...  
Keyword(s):  
Social Trust ◽  
Social Information ◽  
Mobile Services ◽  
User Preference ◽  
User Interest ◽  
Effective Measure ◽  
Trust Relationships ◽  
General Trust ◽  
Implicit Trust ◽  
User Trust

In recent years, with the development of advanced mobile applications, people’s various daily behavior data, such as geographic location, social information, hobbies, are more easily collected. To process these data, data cross-boundary fusion has become a key technology, and there are some challenges, such as solving the problems of the cross-boundary business integrity, cross-boundary value complementarity and so on. Mobile Services Recommendation requires improved recommendation accuracy. User trust is an effective measure of information similarity between users. Using trust can effectively improve the accuracy of recommendations. The existing methods have low utilization of general trust data, sparseness of trust data, and lack of user trust characteristics. Therefore, a method needs to be proposed to make up for the shortcomings of explicit trust relationships and improve the accuracy of user interest feature completion. In this paper, a recommendation model is proposed to mine the implicit trust relationships from user data and integrate the explicit social information of users. First, the rating prediction model was improved using the traditional Singular Value Decomposition (SVD) model, and the implicit trust relationships were mined from the user’s historical data. Then, they were fused with the explicit social trust relationships to obtain a crossover data fusion model. We tested the model using three different orders of magnitude. We compared the user preference prediction accuracies of two models: one that does not integrate social information and one that integrates social information. The results show that our model improves the user preference prediction accuracy and has higher accuracy for cold start users. On the three data sets, the average error is reduced by 2.29%, 5.44% and 4.42%, suggesting that it is an effective data crossover fusion technology.

Conceptions of Trust in the Qur'an

2011 ◽  
Vol 13 (2) ◽  
pp. 56-85 ◽  
Author(s):  
Nora S. Eggen
Keyword(s):  
Social Trust ◽  
Social Ethics ◽  
Religious Discourse ◽  
Human Being ◽  
Human Beings ◽  
Normative Claim ◽  
Trust Relationship ◽  
Trust Relationships ◽  
The Absolute ◽  
The Guardian

In the Qur'an we find different concepts of trust situated within different ethical discourses. A rather unambiguous ethico-religious discourse of the trust relationship between the believer and God can be seen embodied in conceptions of tawakkul. God is the absolute wakīl, the guardian, trustee or protector. Consequently He is the only holder of an all-encompassing trusteeship, and the normative claim upon the human being is to trust God unconditionally. There are however other, more polyvalent, conceptions of trust. The main discussion in this article evolves around the conceptions of trust as expressed in the polysemic notion of amāna, involving both trust relationships between God and man and inter-human trust relationships. This concept of trust involves both trusting and being trusted, although the strongest and most explicit normative claim put forward is on being trustworthy in terms of social ethics as well as in ethico-religious discourse. However, ‘trusting’ when it comes to fellow human beings is, as we shall see, framed in the Qur'an in less absolute terms, and conditioned by circumstantial factors; the Qur'anic antithesis to social trust is primarily betrayal, ‘khiyāna’, rather than mistrust.

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.

scholarly journals Recommendation Model for Trust Circle Mining Based on Users' Interest Fields

2019 ◽  
Vol 37 (6) ◽  
pp. 1294-1301
Author(s):  
Yun Bai ◽  
Wandong Cai
Keyword(s):  
Recommendation System ◽  
Generalized Trust ◽  
Data Set ◽  
Rating Data ◽  
Trust Relationship ◽  
Model Based ◽  
Trust Relationships ◽  
Implicit Trust ◽  
Rating Prediction ◽  
Trust Information

A trust-based recommendation system recommends the resources needed for users by system rating data and users' trust relationship. In current relevant work, an over-generalized trust relationship is likely to be considered without exploiting the relationship between trust information and interest fields, affecting the precision and reliability of the recommendation. This research, therefore, proposes a users' interest-field-based trust circle model. Based on different interest fields, it exploits potential implicit trust relationships in separated layers. Besides, it conducts user rating by combining explicit trust relationships. This model not only considers the matching between trust information and fields, but also explores the implicit trust relationships between users do not revealed in specific fields, thus it is able to improve the precision and coverage of rating prediction. The experiments made with the Epinions data set proved that the recommendation model based on trust circle exploiting in users' interest fields proposed in this research, is able to effectively improve the precision and coverage of the recommendation rating prediction, compared with the traditional recommendation algorithm based on generalized trust relationship.

scholarly journals Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust

2019 ◽  
Author(s):  
Qitian Wu ◽  
Lei Jiang ◽  
Xiaofeng Gao ◽  
Xiaochun Yang ◽  
Guihai Chen
Keyword(s):  
Collaborative Filtering ◽  
Social Trust ◽  
Auxiliary Information ◽  
Feature Space ◽  
User Preference ◽  
Bayesian Optimization ◽  
Task Learning ◽  
Trust Relationships ◽  
Feature Evolution ◽  
User Trust

Social recommendation could address the data sparsity and cold-start problems for collaborative filtering by leveraging user trust relationships as auxiliary information for recommendation. However, most existing methods tend to consider the trust relationship as preference similarity in a static way and model the representations for user preference and social trust via a common feature space. In this paper, we propose TrustEV and take the view of multi-task learning to unite collaborative filtering for recommendation and network embedding for user trust. We design a special feature evolution unit that enables the embedding vectors for two tasks to exchange their features in a probabilistic manner, and further harness a meta-controller to globally explore proper settings for the feature evolution units. The training process contains two nested loops, where in the outer loop, we optimize the meta-controller by Bayesian optimization, and in the inner loop, we train the feedforward model with given feature evolution units. Experiment results show that TrustEV could make better use of social information and greatly improve recommendation MAE over state-of-the-art approaches.

scholarly journals Top-N Recommendation Based on Mutual Trust and Influence

2019 ◽  
Vol 14 (4) ◽  
pp. 540-556 ◽  
Author(s):  
Dewen Seng ◽  
Jiaxin Liu ◽  
Xuefeng Zhang ◽  
Jing Chen ◽  
Xujian Fang
Keyword(s):  
High Efficiency ◽  
Mutual Influence ◽  
Measurement Model ◽  
Mutual Trust ◽  
Trust Relationship ◽  
The Social ◽  
Recommendation Algorithms ◽  
The Difference ◽  
Influence Measurement ◽  
Relationship Of

To improve recommendation quality, the existing trust-based recommendation methods often directly use the binary trust relationship of social networks, and rarely consider the difference and potential influence of trust strength among users. To make up for the gap, this paper puts forward a hybrid top-N recommendation algorithm that combines mutual trust and influence. Firstly, a new trust measurement method was developed based on dynamic weight, considering the difference of trust strength between users. Secondly, a new mutual influence measurement model was designed based on trust relationship, in light of the social network topology. Finally, two hybrid recommendation algorithms, denoted as FSTA(Factored Similarity model with Trust Approach) and FSTI(Factored similarity models with trust and influence), were presented to solve the data sparsity and binarity. The two algorithms integrate user similarity, item similarity, mutual trust and mutual influence. Our approach was compared with several other recommendation algorithms on three standard datasets: FilmTrust, Epinions and Ciao. The experimental results proved the high efficiency of our approach.

scholarly journals A Self-Orgnizing Model for Peer-to-Peer Systems using Trust Relations

2014 ◽  
Vol 14 (1) ◽  
pp. 5394-5397
Author(s):  
Sourabh S. Mahajan ◽  
S.K. Pathan
Keyword(s):  
Peer To Peer ◽  
Local Information ◽  
Global Information ◽  
Trust Network ◽  
Trust Relationship ◽  
The Past ◽  
Trust Relationships ◽  
Peer To Peer Systems ◽  
Information Trustworthiness

Peer-to-Peer systems enables the interactions of peers to accomplish tasks. Attacks of peers with malicious can be reduced by establishing trust relationship among peers. In this paper we presents algorithms which helps a peer to reason about trustworthiness of other peers based on interactions in the past and recommendations. Local information is used to create trust network of peers and does not need to deal with global information. Trustworthiness of peers in providing services can be describedby Service metric and recommendation metric. Parameters considered for evaluating interactions and recommendations are Recentness, Importance and Peer Satisfaction. Trust relationships helps a good peer to isolate malicious peers.

scholarly journals The Creation of Beautiful Countryside in the Internet Age: Semantics, Theory and Path-Taking Yuan Ye as an Example

2021 ◽  
Vol 236 ◽  
pp. 05014
Author(s):  
Dong Yue ◽  
Li Gang ◽  
Wu Zhenzhi
Keyword(s):  
Rapid Development ◽  
Mobile Internet ◽  
Rural Culture ◽  
The Internet ◽  
Cultural Resources ◽  
Local Conditions ◽  
Internet Age ◽  
Urban Rural ◽  
Relationship Of ◽  
Design Ideas

With the advent of the Internet Age, the rapid development of modern rural construction and urban-rural integration, the revival of traditional culture and environmental improvement and many other factors, the multi-semantic social relationship of the beautiful countryside has been formed gradually based on the space, culture and locality. The reconstruction and reshaping of beautiful countryside in the Mobile Internet Age takes Yuan Ye as a theory and path to improve the quality of Internet rural culture, and combines the current technological innovation to expound such design ideas as poetic charm, adaptation to local conditions and so on in garden culture, injecting new design vitality to the inheritance of cultural resources and construction of modern rural society.

A Survey of Trust Use and Modeling in Real Online Systems

2013 ◽  
pp. 167-196
Author(s):  
Paolo Massa
Keyword(s):  
Social Relationships ◽  
Information Overload ◽  
The Internet ◽  
Online Systems ◽  
Trust Relationships ◽  
Different Types ◽  
Concept Of Information ◽  
The Web ◽  
Trust Information

This chapter discusses the concept of trust and how trust is used and modeled in online systems currently available on the Web or on the Internet. It starts by describing the concept of information overload and introducing trust as a possible and powerful way to deal with it. It then provides a classification of the systems that currently use trust and, for each category, presents the most representative examples. In these systems, trust is considered as the judgment expressed by one user about another user, often directly and explicitly, sometimes indirectly through an evaluation of the artifacts produced by that user or his/her activity on the system. We hence use the term “trust” to indicate different types of social relationships between two users, such as friendship, appreciation, and interest. These trust relationships are used by the systems in order to infer some measure of importance about the different users and influence their visibility on the system. We conclude with an overview of the open and interesting challenges for online systems that use and model trust information.

Sign in / Sign up

Export Citation Format

Share Document