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.

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.

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 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.

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.

A Method of Interest Degree Mining Based on Behavior Data Analysis

2019 ◽  
Vol 34 (09) ◽  
pp. 2059030
Author(s):  
Zhen Li ◽  
Shuo Xu ◽  
Tianyu Wang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Fusion ◽  
Language Processing ◽  
User Behavior ◽  
Comprehensive Evaluation ◽  
Mapping Method ◽  
User Preference ◽  
User Interest ◽  
Improvement Measures

Based on big data, this paper starts from the behavior data of users on social media, and studies and explores the core issues of user modeling under personalized services. Focusing on the goal of user interest modeling, this paper proposes corresponding improvement measures for the existing interest model, which has great difference in interest description among different users and it is difficult to find the user interest change in time. For the above problems, this paper takes user-generated content and user behavior information as the analysis object, and uses natural language processing, knowledge warehouse, data fusion and other methods and techniques to numerically analyze user interest mining based on text mining and multi-source data fusion. We propose a user interest label space mapping method to avoid data sparse problem caused by too many dimensions in interest analysis. At the same time, we propose a method to extract and blend the long-term and short-term interests, and realize the comprehensive evaluation of interests. In the analysis of the big data phase, the user preference social property application preference value law, it is expected to achieve user Internet social media application preference data mining from the perspective of big data.

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.

Recommender System with Composite Social Trust Networks

2016 ◽  
Vol 13 (2) ◽  
pp. 56-73 ◽  
Author(s):  
Chaochao Chen ◽  
Xiaolin Zheng ◽  
Mengying Zhu ◽  
Litao Xiao
Keyword(s):  
Online Social Networks ◽  
Prediction Accuracy ◽  
Social Trust ◽  
Convergence Speed ◽  
Trust Network ◽  
True Value ◽  
Trust Networks ◽  
Factorization Model ◽  
Implicit Trust ◽  
Probabilistic Matrix Factorization

The development of online social networks has increased the importance of social recommendations. Social recommender systems are based on the idea that users who are linked in a social trust network tend to share similar interests. Thus, how to build an accurate social trust network will greatly affect recommendation performance. However, existing trust-based recommender approaches do not fully utilize social information to build rational trust networks and thus have low prediction accuracy and slow convergence speed. In this paper, the authors propose a composite trust-based probabilistic matrix factorization model, which is mainly composed of two steps: In step 1, the existing explicit trust network and the inferred implicit trust network are used to build a composite trust network. In step 2, the composite trust network is used to minimize both the rating difference and the trust difference between the true value and the inferred value. Experiments based on an Epinions dataset show that the authors' approach has significantly higher prediction accuracy and convergence speed than traditional collaborative filtering technology and the state-of-the-art trust-based recommendation approaches.

scholarly journals Socio-Economic Modernization and the “Crisis of Trust” in China: A Multi-level Analysis of General and Particular Trust

2020 ◽  
Vol 152 (3) ◽  
pp. 923-949
Author(s):  
H. Christoph Steinhardt ◽  
Jan Delhey
Keyword(s):  
Survey Data ◽  
Social Trust ◽  
Economic Security ◽  
Contextual Effect ◽  
General Trust ◽  
Wide Range ◽  
Multi Level ◽  
The Impact ◽  
Economic Modernization ◽  
Level Analysis

Abstract Theorists have long disagreed about the impact of socio-economic modernization on social trust. The pessimistic school asserts that modernization undermines the structural conditions for high levels of trust. The optimistic account argues that it delivers economic security and human empowerment and thereby enhances trust. Adapting these contrasting theories to the specific case of China, this article puts them to the test with survey data from the World Values Survey. Exploiting the condition of highly uneven levels of regional development, combined with common political institutions and a shared cultural heritage, the study conducts a multi-level analysis of survey data from over 1900 individuals and a wide range of regional statistics from 61 county-level units. While trust in family members and particular trust beyond the family are unaffected by levels of regional modernization, we find robust evidence to suggest that regional modernization is associated with substantially higher levels of general trust. The results further suggests that higher general trust in more developed regions does not lead to an enhanced conversion of particular into general trust. This indicates that general trust is nurtured through the contextual effect of residing in more modern social environments. Overall, these findings provide substantial support for modernization optimists and lend themselves to a reinterpretation of a widely discussed “trust crisis” in China, which to date is often interpreted according to the pessimistic view of modernization.

scholarly journals Manifestation of Compensatory Behavior among Preschool Children with Trust Deprivation

2018 ◽  
Vol 50 ◽  
pp. 01134
Author(s):  
Anzhelika Polina ◽  
Elena Ovcharova
Keyword(s):  
Preschool Children ◽  
Antisocial Behavior ◽  
Social Trust ◽  
Compensatory Behavior ◽  
Active Type ◽  
Trust Relationships ◽  
The World ◽  
The Difference ◽  
Aggressive Reactions

The article considers the problem of personal features formation among preschoolers with deprivation. The article presents the results of research aimed to reveal the peculiarities of the mental deprivation and trust deprivation among orphans, and to compare the results with the children raised in families. Many basic attitudes of the orphans are deformed, in particular, social trust relationships with the world, which are manifested in two forms of compensatory behavior. The first group comprises children with trust deprivation of passive type, characterized by anxiety, shyness, hypochondria, inability to stand up for themselves. The second group comprises children with trust deprivation of active type; these children are outwardly sociable, but they are prone to aggressive reactions, antisocial behavior and conflicts. Despite the difference in the behavior, orphans are characterized by common personal features: insecurity, inferiority, hostility, proneness to conflict, difficulties in communication.

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