scholarly journals Context-Aware Computational Trust Model for Recommender Systems

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Edwin O. Ngwawe ◽  
Elisha O. Abade ◽  
Stephen N. Mburu
Keyword(s):  
Recommender Systems ◽  
Customer Service ◽  
Information Overload ◽  
Trust Model ◽  
Recommendation Algorithm ◽  
Computational Trust ◽  
Work From Home ◽  
Networking Technologies ◽  
The Government ◽  
Potential Customers

With increase in computing and networking technologies, many organizations have managed to place their services online with the aim of achieving efficiency in customer service as well as reach more potential customers, also with communicable diseases such as COVID-19 and need for social distancing, many people are encouraged to work from home, including shopping. To meet this objective in areas with poor Internet connectivity, the government of Kenya recently announced partnership with Google Inc for use of Google Loon. This has come up with challenges which include information overload on the side of the end consumer as well as security loopholes such as dishonest vendors preying on unsuspecting consumers. Recommender systems have been used to alleviate these two challenges by helping online users select the best item for their case. However, most recommender systems, especially common filtering recommendation algorithm (CFRA) based systems still rely on presenting output based on selections of nearest neighbors (most similar users – birds of the same feathers flock together). This leaves room for manipulation of the output by mimicking the features of their target and then picking malicious item such that when the recommender system runs, it will output the same malicious item to the target – a trust issue. Data to construct trust is equally a challenge. In this research, we propose to address this issue by creating a trust adjustment factor (TAF) for recommender systems for online services.

Two Phase Recommendation Algorithm Based on Clustering User Interest and Trust

2014 ◽  
Vol 543-547 ◽  
pp. 1856-1859
Author(s):  
Xiang Cui ◽  
Gui Sheng Yin
Keyword(s):  
Social Networks ◽  
Electronic Commerce ◽  
Recommender Systems ◽  
Information Overload ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Two Phase ◽  
Rating Data ◽  
User Interests ◽  
The Future

Recommender systems have been proven to be valuable means for Web online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. We need a method to solve such as what items to buy, what music to listen, or what news to read. The diversification of user interests and untruthfulness of rating data are the important problems of recommendation. In this article, we propose to use two phase recommendation based on user interest and trust ratings that have been given by actors to items. In the paper, we deal with the uncertain user interests by clustering firstly. In the algorithm, we compute the between-class entropy of any two clusters and get the stable classes. Secondly, we construct trust based social networks, and work out the trust scoring, in the class. At last, we provide some evaluation of the algorithms and propose the more improve ideas in the future.

scholarly journals Fusion of Internal Similarity to Improve the Accuracy of Recommendation Algorithm

2022 ◽  
Vol 24 (1) ◽  
pp. 139-140
Author(s):  
Dr.S. Dhanabal ◽  
◽  
Dr.K. Baskar ◽  
R. Premkumar ◽  
◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Overload ◽  
Real Life ◽  
Similarity Matrix ◽  
Recommendation Algorithm ◽  
Simulation Experiments ◽  
Average Similarity ◽  
Recommendation Accuracy ◽  
Item Item

Collaborative filtering algorithms (CF) and mass diffusion (MD) algorithms have been successfully applied to recommender systems for years and can solve the problem of information overload. However, both algorithms suffer from data sparsity, and both tend to recommend popular products, which have poor diversity and are not suitable for real life. In this paper, we propose a user internal similarity-based recommendation algorithm (UISRC). UISRC first calculates the item-item similarity matrix and calculates the average similarity between items purchased by each user as the user’s internal similarity. The internal similarity of users is combined to modify the recommendation score to make score predictions and suggestions. Simulation experiments on RYM and Last.FM datasets, the results show that UISRC can obtain better recommendation accuracy and a variety of recommendations than traditional CF and MD algorithms.

A Collaborative Filtering Recommendation Algorithm Based on Product Clustering

2012 ◽  
Vol 267 ◽  
pp. 87-90
Author(s):  
Pu Wang
Keyword(s):  
Electronic Commerce ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommendation System ◽  
Information Overload ◽  
Personalized Recommendation ◽  
The Internet ◽  
Recommendation Algorithm ◽  
The Core ◽  
Intelligent Technology

E-commerce recommendation system is one of the most important and the most successful application field of information intelligent technology. Recommender systems help to overcome the problem of information overload on the Internet by providing personalized recommendations to the customers. Recommendation algorithm is the core of the recommendation system. Collaborative filtering recommendation algorithm is the personalized recommendation algorithm that is used widely in e-commerce recommendation system. Collaborative filtering has been a comprehensive approach in recommendation system. But data are always sparse. This becomes the bottleneck of collaborative filtering. Collaborative filtering is regarded as one of the most successful recommender systems within the last decade, which predicts unknown ratings by analyzing the known ratings. In this paper, an electronic commerce collaborative filtering recommendation algorithm based on product clustering is given. In this approach, the clustering of product is used to search the recommendation neighbors in the clustering centers.

Document recommendation based on the analysis of group trust and user weightings

2019 ◽  
Vol 45 (6) ◽  
pp. 845-862 ◽  
Author(s):  
Chin-Hui Lai ◽  
Yu-Chieh Chang
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Overload ◽  
Trust Model ◽  
Or Groups ◽  
Personal Trust ◽  
Knowledge Intensive ◽  
Group Trust ◽  
Support Work

Collaborative filtering (CF) has been applied in various domains to resolve problems related to information overload. In a knowledge-intensive environment, most works are processed through teamwork. A user on a team can reference task-related documents from other trusted members to support work on the task. However, the traditional personalised recommender systems no longer meet the demand of teams or groups. Therefore, this work proposes a novel document recommendation method based on a group-based trust model. Our method will analyse the degrees of trust among users in a group and then identify the trustworthy users. The proposed group trust consists of a hybrid personal trust (HPT) model and users’ importance (i.e. users’ activity, similarity and reputation) in a group. Group-based trust is then integrated with the user-based CF to recommend documents to users. The experiments demonstrate that the proposed method can provide better performance than other trust-based recommendation methods; it not only obtains reliable trust values to increase the accuracy of predictions but also enhances the recommendation quality.

The Use of Social Media by the Botswana Government and Governance of Liquid Communication

Mousaion ◽  
2019 ◽  
Vol 37 (1) ◽  
Author(s):  
Tshepho Lydia Mosweu
Keyword(s):  
Social Media ◽  
Customer Service ◽  
Community Involvement ◽  
Media Content ◽  
Service Access ◽  
Communication Tool ◽  
African Countries ◽  
The World ◽  
The Government ◽  
Use Of Social Media

Social media as a communication tool has enabled governments around the world to interact with citizens for customer service, access to information and to direct community involvement needs. The trends around the world show recognition by governments that social media content may constitute records and should be managed accordingly. The literature shows that governments and organisations in other countries, particularly in Europe, have social media policies and strategies to guide the management of social media content, but there is less evidence among African countries. Thus the purpose of this paper is to examine the extent of usage of social media by the Botswana government in order to determine the necessity for the governance of liquid communication. Liquid communication here refers to the type of communication that goes easily back and forth between participants involved through social media. The ARMA principle of availability requires that where there is information governance, an organisation shall maintain its information assets in a manner that ensures their timely, efficient and accurate retrieval. The study adopted a qualitative case study approach where data were collected through documentary reviews and interviews among purposively selected employees of the Botswana government. This study revealed that the Botswana government has been actively using social media platforms to interact with its citizens since 2011 for increased access, usage and awareness of services offered by the government. Nonetheless, the study revealed that the government had no official documentation on the use of social media, and policies and strategies that dealt with the governance of liquid communication. This study recommends the governance of liquid communication to ensure timely, efficient and accurate retrieval when needed for business purposes.

An Overview of Collaborative Filtering Recommendation Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 3899-3903
Author(s):  
Ping Sun ◽  
Zheng Yu Li ◽  
Zi Yang Han ◽  
Feng Ying Wang
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Evaluation Method ◽  
Decisive Role ◽  
Classical Solutions ◽  
Critical Problem ◽  
Recommendation Algorithm ◽  
Data Sparseness ◽  
Collaborative Filtering Algorithm ◽  
And Performance

Recommendation algorithm is the most core and key point in recommender systems, and plays a decisive role in type and performance evaluation. At present collaborative filtering recommendation not only is the most widely useful and successful recommend technology, but also is a promotion for the study of the whole recommender systems. The research on the recommender systems is coming into a focus and critical problem at home and abroad. Firstly, the latest development and research in the collaborative filtering recommendation algorithm are introduced. Secondly, the primary idea and difficulties faced with the algorithm are explained in detail. Some classical solutions are used to deal with the problems such as data sparseness, cold start and augmentability. Thirdly, the particular evaluation method of the algorithm is put forward and the developments of collaborative filtering algorithm are prospected.

The use and non-use of the government financial report by Maltese Members of Parliament

2018 ◽  
Vol 31 (4) ◽  
pp. 1124-1144 ◽  
Author(s):  
Josette Caruana ◽  
Brady Farrugia
Keyword(s):  
Decision Making ◽  
Information Overload ◽  
Qualitative Interviews ◽  
Financial Reports ◽  
Content Type ◽  
Financial Report ◽  
Members Of Parliament ◽  
The Government ◽  
And Performance ◽  
Questionnaire Research

Purpose The purpose of this paper is to examine the use and non-use of the Government Financial Report by Maltese Members of Parliament (MPs). It refers to information overload theory to analyse the gap between financial reports and their relevance for decision making. Design/methodology/approach A mix of qualitative (interviews) and quantitative (questionnaire) research tools are applied, with the Maltese MPs being the research participants. This method is acclaimed to be comprehensive, but this study highlights certain disadvantages when applied in the political arena. Findings The characteristics of the information itself could be the main cause of information overload, resulting in the non-use of the financial report for decision making. Politicians refer to financial data for their decision making, but not to the data presented in the financial report. Irrespective of the politician’s professional background, the data in the financial report is perceived as incomplete and outdated. Practical implications The cause of information overload and its effects are important considerations for preparers of financial information and accounting standard setters, if they wish that their production is relevant for decision makers. Originality/value There is an increase in research concerning politicians’ use of budgetary and performance information, at local and regional levels of government. This study investigates exclusively the use of the financial report by politicians at central level, in a politically stable environment.

scholarly journals THE INFLUENCE OF ERGONOMIC ON WORKING FROM HOME ACTIVITIES

2020 ◽  
Vol 5 (1) ◽  
pp. 58
Author(s):  
Imtihan Hanom ◽  
Rachel Aleyda Rozefy ◽  
Hilmiyani Taqiyyah Filasta
Keyword(s):  
Virus Transmission ◽  
Physical Distance ◽  
Employee Safety ◽  
Work From Home ◽  
Corona Virus ◽  
Working From Home ◽  
Short Period ◽  
Long Time ◽  
The Government ◽  
High Flexibility

Work From Home (WFH) is a system chosen by the government in 2020 due to the spread of the Corona virus, with this system it is hoped that it can maintain social distance, namely reducing people's mobility, maintaining physical distance, and reducing crowds so that it is expected to reduce the risk of corona virus transmission. and employee safety. The WFH system has high flexibility, this is to support employee balance between work and life. The work system that changed to WFH in a short period of time made workers experience stressful conditions such as feelings of anxiety or worry for a long time, especially when they lived under the same roof with many people. In carrying out WFH, workers need a comfortable place to work to help focus on work. One of the things that play a role in creating a sense of comfort when working is the application of ergonomic rules. This study looks for any variables that can affect WFH activities and which variables most affect WFH activities. The application of ergonomics, especially macro ergonomics in WFH activities, is considered appropriate to be able to solve various problems in WFH activities. This study uses a descriptive qualitative method by conducting a study through distributing questionnaires to respondents who are doing WFH. From the results of the study, it was found that the comfort of workers in carrying out WFH activities is closely related to ergonomics in a residential house. The results of this study can be used as a reference for designing a suitable workspace for WFH activities, and as a reference for further research with a similar focus of study. Keyword: Interior, Ergonomic, Working From Home

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