Data, information and knowledge management in the field of smart business
Fatemeh Rezaimehr; Chitra Dadkhah
Abstract
AbstractRecently, the Internet has played a significant and substantial role in people's lives. However, the content available in the global web environment should align with users' daily needs, providing them with useful and up-to-date information tailored to their tastes. In this context, recommender ...
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AbstractRecently, the Internet has played a significant and substantial role in people's lives. However, the content available in the global web environment should align with users' daily needs, providing them with useful and up-to-date information tailored to their tastes. In this context, recommender systems assist users by suggesting items that closely match their preferences in less time. Today, with the exponential growth of data, the utilization of recommender systems has surged. Conversely, these systems encounter challenges such as evolving user preferences over time, cold start problem, sparsity within the user-item matrix, the infiltration of fake users in the systems, and their adverse impact on the recommendation lists. The objective of this paper is to propose a recommender system grounded in time and trust factors to enhance the efficiency and precision of system recommendations. Initially, the proposed system addresses the data sparsity dilemma by incorporating reliable implicit ratings into the user-item matrix. Subsequently, it constructs a weighted user-user network based on user rating timestamps and trust relationships among users, thereby mitigating the cold start problem and accounting for changing user preferences over time. The proposed recommender system employs a novel community detection algorithm introduced in this paper to identify the nearest neighbors of active users and recommends the top @k items based on the collaborative filtering approach. Evaluation results of the proposed system, tested on a film recommender system using the Epinions dataset, demonstrate its superior efficiency compared to basic systems.IntroductionToday, with the increasing tendency of users to use websites for obtaining information, online shopping, and using social networks for expressing personal opinions, the ways of obtaining information and establishing connections among users have undergone significant changes. Consequently, users are confronted with the big of data. Managing this data and selecting the appropriate options from this vast collection and presenting it to users is one of the main reasons for the development of information retrieval systems and search engines. In this regard, Recommendation Systems (RSs) help users choose the best options and recommend items that are closer to their preferences in the shortest possible time. Different models of RS such as collaborative filtering, content-based, knowledge-based, and newly developed context-aware RS, have been presented by researchers (Casillo et al., 2022). Each has its own advantages and disadvantages, which can be combined to create a hybrid RS. It should be noted that RS face challenges, including changes in user preferences over time, cold start for new users or items, sparsity of the user-item matrix, attack by fake users, and their negative impact on the recommendation list. In this paper, a time- and trust-based recommendation system is presented to enhance the performance and accuracy of recommendations. Our proposed system initially solves the data sparsity problem by adding reliable implicit ratings to the user-item rating matrix. It then generates a weighted user-user network based on the time of user feedback on items and trust relationships among users. This approach addresses the cold start problem and the change in user preferences over time. Our system is based on a novel community detection algorithm presented in this article, which identifies the nearest neighboring users with similar tastes to the active user and recommends the top-k items using the collaborative filtering method. The evaluation of the proposed system is performed on an Epinions dataset for a movie recommendation system. The evaluation uses metrics such as accuracy, recall, F1 score, mean absolute error, and root mean square error. The experimental results indicate the superior performance of the proposed system compared to similar systems.Literature ReviewIn the recent years, the researchers attempt to improve the accuracy of their recommendation for retaining the users and increasing the profit. Some of the papers has worked on optimizing the performance of their proposed RS using evolutionary algorithms (Tohidi & Dadkhah, 2020) and the others used the additional information such as time, location, etc. Trust-based RSs have been recently introduced to the community of computer science. Recent studies have shown that incorporating social factors or trust statements in RSs leads to the improvement of recommendation quality (P. Moradi & Ahmadian, 2015; S. Ahmadian, M. Meghdadi, & Afsharchi, 2018b). So far, several trust-based CF approaches have been proposed to overcome data sparsity and cold-start problems as well as to increase recommendable items (Ghavipour & Meybodi, 2016; Moradi, Ahmadian, & Akhlaghian, 2015; P. Massa & Avesani, 2007; Ranjbar Kermany & Alizadeh, 2017). Trust statements can be explicitly collected from users or can be implicitly inferred from users behaviors (S. Ahmadian, M. Meghdadi, & Afsharchi, 2018a; S. Ahmadian, P. Moradi, & Akhlaghian, 2014). Liu and Lee proposed a specific approach which does not directly use the trust information; instead they take into account the number of exchanged messages among the users of the system to construct the trust network (Liu & Lee, 2010). Alahmadi and Zeng presented a framework to apply short texts posted by users friends in microblogs as an additional data source to build the trust network (Alahmadi & Zeng, 2015). Since explicit trust statements are directly specified by the users, they are more accurate and reliable than implicit ones in determining social relationships among users (Cho, Kwon, & Park, 2009; Ingoo, Kyong, & Tae, 2003; Lathia, Hailes, & Capra, 2008; Manolopoulus, Nanopoulus, Papadopoulus, & Symeonidis, 2008).The research In (Abdul-Rahman & Hailes, 2000) has been shown that a user constructs his/her social connections with someone who has similar tastes. Massa and Avesani showed that adding social network data to traditional collaborative filtering improves the recommendation results (P. Massa & Avesani, 2007). Gharibshah and Jalili studied the relation between RSs and connectedness of users-items bipartite interaction network (Gharibshah & Jalili, 2014). Guo et al. proposed a method which merged the ratings of users trusted neighbors with the other information sources to identify their preferences (G. Guo, J. Zhang, & Thalmann, 2014). Yang et al. proposed a Bayesian inference based recommendation method for online social networks (X. Yang, Y. Guo, & Liu, 2013). In this method, the similarity value between each pair of users is measured using a set of conditional probabilities derived from their mutual ratings. Jiang et al. introduced a framework to incorporate interpersonal influences of users in social network with their individual preferences to improve the accuracy of social recommendation (Jiang, Cui, Wang, Zhu, & Yang, 2014).Purchase/rating time is one of the most important contextual information that can be used to design RSs with high precision (Xiong, Chen, Huang, Schneider, & Carbonell, 2010). The main motivation for time-aware RS is that in realistic scenarios users tastes might change over time.MethodologyWe propose a time and trust-aware RS using a graph-based community detection method consists of four steps: 1: developing a user-item rating matrix, 2: constructing a time weighted user-user network, 3: performing graph- based community detection, 4: recommending Top-N items. In the first step, the user-item rating matrix is developed by adding some implicit ratings and the quality of the implicit ratings is evaluated using a reliability measurement. In the second step, a time-weighted user-user network is constructed based on the combination of trust relationships and similarity between users. Moreover, the timestamps of user-item ratings are considered to calculate the similarity between users. In the third step, a graph-based community detection method classifies similar users into appropriate communities. Finally, in the fourth step, it predicts the rating for each unobserved item and top-N recommendations is generated for the target user.We proposed a new community detection method that consists of three phases. First, the initial centers of communities are obtained using a sparsest subgraph of weighted user-user network. It should be noted that the initial centers must have the maximum dissimilarities with each other based on the general concept of clustering and community detection algorithms. Then users can be assigned to their nearest communities. For each user proposed system calculated the fitness function. User has associated to community which has high value of fitness function. Then the centers of communities were updated in order to maximize a fitness function. This process is iteratively repeated until members of communities do not change and steady state is achieved. A set of communities are identified where the users are assigned to their corresponding communities. Some of the communities may have overlap and they can be merged. The final communities were used as the nearest neighbors set of the active user in the same community for the recommendation.ConclusionOur proposed algorithm solves the sparsity of rating matrix by adding the implicit rating and solved cold-start problem for new users by considering the trust between the users. We applied the proposed algorithm on extended Epinions dataset and compared its performance with similar algorithms. The experimental results showed that our proposed algorithm outperforms the other algorithms according to the accuracy and recommends the top@N items with high precision.
Vahideh Alipoor; Mohammad Reza Sa'di; Ghazal Golshan
Abstract
Nowadays, one of the important topics in e-commerce which has become a concern of many countries is electronic word of mouth due to increasing use of Internet for activities such as shopping or obtaining information. The purpose of this study is to investigate the effect of e-service factors as well ...
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Nowadays, one of the important topics in e-commerce which has become a concern of many countries is electronic word of mouth due to increasing use of Internet for activities such as shopping or obtaining information. The purpose of this study is to investigate the effect of e-service factors as well as experience and risk on E-WOM by considering the mediating role of the trust variable. The statistical population of this research consists of buyers ftom Tehran/Karaj from Alibaba website. 384 samples were selected by Cochran's formula and data was collected by distributing questionnaires. The research data were analyzed by structural equation modeling using Amos. The results show that e-services dimensions as well as experience have a positive and significant effect on trust and E-WOM. Trust also has positive and significant effect on E-WOM. The results emphasize the importance of focusing on improving the quality of e-services in online stores and user experience of buyers on their participation in advertising.
Samad Rahimiaghdam; Alireza Fazlzadeh; Noushin Ebrahimiaghdam
Abstract
The aim of this research is to explain the scientific foundation and offer practical solutions for online purchase intonation based on assurance strategies by mediating role of trust in online stores. The research is applied from goal perspective and descriptive -correlative from data collection type. ...
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The aim of this research is to explain the scientific foundation and offer practical solutions for online purchase intonation based on assurance strategies by mediating role of trust in online stores. The research is applied from goal perspective and descriptive -correlative from data collection type. The statistical population is all people of Tabriz city who have had at least one Internet shopping experience. 384 samples were selected by random sampling. Standard questionnaires were used to collect data. The data were analyzed by structural equations modeling method with AMOS software.The research findings indicate, assurance statement has directly effect with path coefficient 0.4 and indirectly effect by the mediating role of trust with path coefficient 0.07 on online purchase intenation. Third party assurance has directly effect with path coefficient of 0.3and indirectly effect by the mediating role of trust with path coefficient 0.08 on online purchase intention. Also trust has a positive impact on online purchase intenation with path coefficient 0.37.Given the significant direct and indirect effect, trust plays a partial mediating role in the relationship between assurance strategies and online purchase intention. It is suggested that managers implement assurance strategies in online stores in order to gain more trust and online purchase intention.
Leila Ebrahimi; Vahid Reza Mirabi; Mohammad Hossein Ranjbar; Esmaeil Hassan Pour
Abstract
The main objective of this research is to provide a customer loyalty model for e-commerce recommender systems. The proposed model is developed using Delone and McLean Information System success model and a set of factors which are identified from the literature. To test the research hypotheses of the ...
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The main objective of this research is to provide a customer loyalty model for e-commerce recommender systems. The proposed model is developed using Delone and McLean Information System success model and a set of factors which are identified from the literature. To test the research hypotheses of the developed model, a questionnaire survey is conducted and the data is collected from the 384 customers in a B2C website. We used SPSS and SmartPLS software for descriptive statistics and path analyses and to verify the proposed model. The result of the Structural Equations Modeling showed that trust has a significant relationship with the customers’ satisfaction in the e-commerce recommendation systems. In addition, the results revealed that satisfaction with the recommended products can improve the customers’ loyalty in the B2C recommendation systems. The proposed model will help the e-commerce managers to improve their website recommendation systems and increase the sale of the products by achieving the customers’ loyalty in the online shopping websites.
Alireza Ghanada; Abouzar Arabsorkhi; Samaneh Rabbani
Abstract
Purpose of this study is to provide a trust model in E-banking in Iran. Concentrating on trust and presenting a trust model are since innovative side of this study. So, first the existing trust models in e-commerce and e-banking have been studied.The proposed model has seven main parts and eight ...
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Purpose of this study is to provide a trust model in E-banking in Iran. Concentrating on trust and presenting a trust model are since innovative side of this study. So, first the existing trust models in e-commerce and e-banking have been studied.The proposed model has seven main parts and eight theories. This model has been tested with electronic questionnaire on the sample of 227 people, including E-banking users. To analyze the data and determine the relationship between variables, Confirmatory factor analysis and PLS software is used. Data analysis in two parts. In The measurement part, technical characteristics of questionnaire including reliability, convergent validity and divergent were examined. In The structure part, structural Coefficients of model have been examined for evaluating hypotheses of study. After the studies, three theories have been rejected from the total of eight and the remaining theories have been accepted. The rejected theories include the relationship between perceive privacy, propensity to trust and perceived competency with trust. P
Hamed Dehghanan; Puya Poormoghadasian
Abstract
One of the positive achievements of information technology in organizations is virtual teams. By taking advantage of IT,virtual teams have greatly facilitated organizational operations. One of the important issues that virtual teams are faced with is trust. The purpose of this study is to investigate ...
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One of the positive achievements of information technology in organizations is virtual teams. By taking advantage of IT,virtual teams have greatly facilitated organizational operations. One of the important issues that virtual teams are faced with is trust. The purpose of this study is to investigate the relationship between trust and effectiveness of virtual teams with emphasis on the role of knowledge sharing. The population of this research is virtual teams working in Tebyan Cultural Institute of Islamic Propaganda Organization. The number of 40 virtual teams comprising 200 employees were selected randomly and data was collected by a questionnaire. Analysis of data through path analysis was performed using Amos software. Path analysis reveals that when knowledge sharing was in appropriate condition, the effect of trust component on the effectiveness of virtual teams would be increased.
Mohammad taghi Taghavifard; Masoumeh Hajian; Elham Poursayyah; Morteza Tahhan
Volume 3, Issue 10 , December 2015, , Pages 58-77
Abstract
Today's, The Progressive Organizations Have Realized The Importance Of The Knowledge And Make Effort To Provide The Present Knowledge In Organization for Those personnel Who Need It. Sharing The Knowledge Is One Of The Most Crucial Issues For The Organizations; In the study of knowledge sharing, trust ...
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Today's, The Progressive Organizations Have Realized The Importance Of The Knowledge And Make Effort To Provide The Present Knowledge In Organization for Those personnel Who Need It. Sharing The Knowledge Is One Of The Most Crucial Issues For The Organizations; In the study of knowledge sharing, trust as one of the factors that will be considered. The Purpose of This Study Was to Investigate the Role of the Organizational Trust in Sharing the Knowledge Between the personnel Of the Islamic Azad University, Tehran Science & Technology Branch in Year 2014. For This Reason, Among 170 Persons Of The Statistical Society, 120 Persons Have Been Selected By Cochran Sampling Formula. The Research Methodology Of This Study Is A Measuring Research, And The Gathering Data Tools Are Questionnaire. To Analyze The Data, The Methods Of The Descriptive, Inferential Statistics And Also Multiple Regressions (by SPSS Software) Have Been Used. The Results Showed That There Is a Positive And Significant Relationship Between Organizational Trust (Horizontal, Vertical & Institutional Trust) And Sharing The Knowledge In Tehran Science & Technology Branch. It Also Became Distinct That The Component Of Horizontal Trust Has The Highest Correlation Coefficient With Respect To The Sharing, The Knowledge And The Vertical Trust Component have The Lowest Correlation Coefficient With It.
