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.

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 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 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 A Comprehensive Social Matrix Factorization with Social Regularization for Recommendation Based on Implicit Similarity by Fusing Trust Relationships and Social Tags

2021 ◽  
Author(s):  
Rui Chen ◽  
Jianwei Zhang ◽  
Zhifeng Zhang ◽  
Yan-Shuo Chang ◽  
Jingli Gao ◽  
...  
Keyword(s):  
Social Relationships ◽  
Social Relationship ◽  
Recommendation System ◽  
Feature Space ◽  
Experimental Results ◽  
User Preference ◽  
Superior Performance ◽  
Social Recommendation ◽  
Social Tags ◽  
Trust Relationships

Abstract Social relationships play an important role in improving the quality of recommender systems (RSs). A large number of experimental results show that social relationship-based recommendation methods alleviate the problems of data sparseness and cold start in RSs to some extent. However, since the social relationships between users are extremely sparse and complex, and it is difficult to obtain accurately user preference model, thus the performance of the recommendation system is affected by the existing social recommendation methods. In order to accurately model social relationships and improve recommendation quality, we use explicit social relationships such as user-item ratings, trust relationships and implicit social relationships such as social tags to mine potential interest preferences of users and propose an improved social recommendation method integrating trust relationship and social tags. The method map user features and item features to the shared feature space by using the above social relationship, respectively, and obtains user similarity and item similarity through potential feature vectors of users and items, and continuously trains them to obtain accurate similarity relationship to improve the recommendation performance. Experimental results demonstrate that our proposed approach achieves superior performance to the other social recommendation approaches.

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 Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

2021 ◽  
Vol 11 (12) ◽  
pp. 5416
Author(s):  
Yanheng Liu ◽  
Minghao Yin ◽  
Xu Zhou
Keyword(s):  
Collaborative Filtering ◽  
User Preference ◽  
Computation Method ◽  
Group Recommendation ◽  
Preference Rating ◽  
Filtering Method ◽  
Personal Recommendation ◽  
Collaborative Filtering Algorithm ◽  
Group Members ◽  
Individual Recommendation

The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

scholarly journals Syn Rec: A Prediction Technique using Collaborative Filtering and Synergy Score

2019 ◽  
Vol 8 (5S3) ◽  
pp. 457-463
Keyword(s):  
Social Interactions ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Effective Technique ◽  
Novel Technique ◽  
Trust Relation ◽  
Prediction Technique ◽  
User Trust

Recommender systems are techniques designed to produce personalized recommendations. Data sparsity, scalability cold start and quality of prediction are some of the problems faced by a recommender system. Traditional recommender systems consider that all the users are independent and identical, its an assumption which leads to a total ignorance of social interactions and trust among user. Trust relation among users ease the work of recommender systems to produce better quality of recommendations. In this paper, an effective technique is proposed using trust factor extracted with help of ratings given so that quality can be improved and better predictions can be done. A novel-technique has been proposed for recommender system using film-trust dataset and its effectiveness has been justified with the help of experiments.

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