• The Journal of Society for e-Business Studies
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• v.14 no.1
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• pp.1-11
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• 2009

Many neighbors are needed for making the recommendation quality better and stable in collaborative filtering. Furthermore, the quality is not so good mainly due to a reason that high similarity between two users does not guarantee the same preference to items considered for recommendation. Dissimilar users who have consistency in item selection can be useful for predicting preferences. This paper proposes a new collaborative filtering method, defining trust based on consistency for improving this phenomenon. Empirical studies show that such a method reduces the number of neighbors required to make the recommendation quality stable and the recommendation quality itself is also significantly improved.

• Culinary science and hospitality research
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• v.23 no.4
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• pp.66-73
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• 2017

This study was performed to verify the relationships among selection attribute, restaurant trust, experiential value, and recommendation focusing on sport center consumers. The data were collected from visitors who registered more than three months in the sport center in South Korea. Total 500 survey was distributed and 330 participants were used for further statistical analysis. SPSS 23.0 and AMOS 21.0 for Windows were used for statistical analysis. Five factors of selection attribute (menu, interior, exterior, staff, convenience) were extracted, and measured by using 15 questions. According to the results of this study, interior, exterior, and staff factors have positive effects on restaurant trust, and interior and menu were significant predictors of the experiential value. In addition, present study confirmed the theoretical relationship among trust, experiential value, and recommend intention as perceived by sport center visitors. Although there are many studies which demonstrated the relationships among selection attribute and other outcome variables, little research explained the relationships among the variables from sport center consumers. Therefore, this study will contribute to provide meaningful results and some practical implications for both academia and the related foodservice industry.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• Journal of Distribution Science
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• v.14 no.6
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• pp.53-63
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• 2016

Purpose - This study examines the effects of the influence strategies on sales representative and headquarter trust, and investigates how sales representative trust plays a mediating role in the relationship between influence strategies and headquarter trust. For these purposes, a structural model which consists of several constructs was developed. In this model, influence strategies that consist of relational influence strategies (information exchange, recommend, promise) and mandatory influence strategies (legal plea, request, threat) were proposed to affect the sales representative trust and in turn, increase the headquarter trust. Thus, this study proposed that sale representative trust plays a core mediating role in the relationship between relational and mandatory influence strategies and headquarter trust in B2B food materials distribution context. Research design, data, and methodology - For these purposes, the authors collected the data from 208 B2B specialized complex agents. We used the 2,200 B2B specialized complex agents which trade with CJ, Ottogi, and Daesang firms and supply food materials to restaurant, school cafeteria, supermarket and traditional market as a sample frame. Once we identified 330 B2B specialized complex agent owners, CEOs, and/or Directors who had agreed to participate in this study, we dropped off a questionnaire at each B2B specialized complex agent and explained the purpose of this study. The survey was conducted from October 1, 2015 to December 15, 2015. A total of 230 questionnaires were collected. Of these collected questionnaires, 28 questionnaires excluded since they had not been fully completed. The data were analyzed using frequency test, reliability test, measurement model analysis, and structural equation modeling with SPSS and SmartPLS 2. Results - First, information exchange, recommendation, and promise of relational influence strategies had positive effects on sales representative trust. The threat of mandatory influence strategies had a negative effect on sales representative trust, but legal plea and request did not have a significant effect on sales representative trust. Second, information exchange and recommendation of relational influence strategies had positive effects on headquarter trust, but promise did not. Also, legal plea, request, and threat of mandatory influence strategies did not have a significant effect on headquarter trust. Third, this findings show that sales representative trust plays a partial mediator between information exchange and headquarter trust, and threat and headquarter trust, and a full mediator between promise and headquarter trust, and recommendation and headquarter trust. Conclusions - The aim of this study was to examine the effects how diverse dimensions of relational and mandatory influence strategies relate to sales representative trust and headquarter trust. To do so, we integrated the influence strategies and the trust transfer theory to hypothesize that various influence strategies increase sales representative and headquarter trust. The findings of this study suggest that headquarter firms should establish and enforce proper influence strategies guidelines to make clear what proper actions sales representatives should implement in relationship with B2B specialized complex agents. Also, relational and mandatory influence strategies must be regarded as a long-term and ongoing strategy that eventually build a long-term orientation with B2B specialized complex agents and guarantee a company's sustainable growth and success.

• Management & Information Systems Review
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• v.35 no.1
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• pp.253-271
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• 2016

This study is to investigate the impact of the social capital on organization trust, and the impact of the organization trust on recommendation intention, and turnover intention. And by this it is also to integrally analyze through what route social capital affects the recommendation intention and turnover intention. An actual analysis through covariance structural equation model was made targeting the members of small and medium sized manufacturing companies. The results of the actual analysis showed that the relational dimension in the social capital had an positive(+) and the most pervasive effect on the organization trust. Relational dimension refers to the formation relationships among members and has a significant value in the interaction in the relation between subordinates and superiors, between colleagues, and between departments. Secondly, the cognitive dimension in the social capital was revealed to have no significant effect on the organization trust and structural dimension was revealed to have a positive(+) effect on the organization trust. Structural dimension refers to the capital value which shows itself in the social network and relationship existing between the members and is formed through building the best network within an organization. Thirdly, organization trust was revealed to have a positive(+) impact on the recommendation intention and to have a negative(-) impact on the turnover intention. Finally, the summary, implications, limitations, and future research direction of this study were presented.

• Culinary science and hospitality research
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• v.22 no.4
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• pp.181-191
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• 2016

This study examined the factors that affect the relationship between eco-friendly agricultural products selection criteria, consumer trust, and recommendation intention. A total of 220 questionnaires were distributed to consumers, of which 210 were deemed suitable for analysis after the removal of 10 unusable responses. In order to perform statistical analyses required in the study, SPSS 18.0 Statistical Program was employed for frequency analysis, factor analysis, reliability analysis, correlations, and regression analysis. The results of exploratory factor analysis showed that four factors regarding eco-friendly agricultural products were extracted from all measurements with a KMO of 0.735 and a total cumulative variance of 79.373%, With regard to consumer trust, one factor was extracted with a total cumulative variance of 75.431% and a KMO score of 0.695. One factor for recommendation intention was extracted that accounted for a total cumulative variance of 68.428% and a KMO score of 0.694. All factors were significant to .000 and the correlation between variables was significant. Thus, based on the results, the main research hypotheses that identify the relationships between selection criteria, consumer trust and recommendation retention were adopted.

• Asia pacific journal of information systems
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• v.33 no.4
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• pp.1156-1170
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• 2023

Various corporates have launched products and brands to meet the diverse needs of consumers. However, an excess of information and products could be collapsed into a choice paradox far from the intention. As an alternative to these problems, curation-based services have recently been in the limelight and can be adopted into e-commerce sites. As one of the earlier studies on the considerable factors of curation services, this study focuses on the online fashion recommendation system in which design quality, interactivity, and perceived usefulness will affect trust, satisfaction, and continued intention to use. In the result, the design quality factor shows a positive effect on satisfaction, but not on the trust. Also, interactivity doesn't demonstrate a significant positive effect on both the satisfaction and the trust factors far from the previous ones but the perceived usefulness had a positive effect on those. In addition, the personalization does not affect a significant effect on the satisfaction factor but on the trust factor. Subsequently, the trust affects the satisfaction, and the satisfaction on the continued intention to use factor, but trust does not significantly affect the continued intention to use directly.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.2
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• pp.53-69
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• 2007

Internet shopping websites have offered comfort to consumers in shopping and built trust relationships with them by providing electronic agents for recommendation, escrow services, and customer centers etc. But as there is little big difference among the shopping websites in terms of technical competence, website design, operational policy, they recognize online feedback (reviews or recommendation of consumers or experts) and online feedback mechanism as important marketing tools. Based on online feedback related studies, this study explores antecedents (consensus, vividness of reviews, interactions in review boards) of the perceived effectiveness of review boards which are text-based feedback mechanisms and its consequences such as trust, satisfaction, and intention to use. The results show that the perceived effectiveness of review boards is significantly affected by vividness of reviews and interactions in review boards, and the impact of interaction in review boards on the perceived effectiveness of review boards is stronger than that of vividness of reviews. The results also show that the perceived effectiveness of review boards has a significant influence on trust and satisfaction with the shopping websites, and intention to use is influenced by both trust and satisfaction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4892-4911
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• 2015

Trust as one of important social relations has attracted much attention from researchers in the field of social network-based recommender systems. In trust network-based recommender systems, there exist normally two roles for users, truster and trustee. Most of trust-based methods generally utilize explicit links between truster and trustee to find similar neighbors for recommendation. However, there possibly exist implicit correlations between users, especially for users with the same role (truster or trustee). In this paper, we propose a novel Collaborative Filtering method called CF-TC, which exploits Trust Context to discover implicit correlation between users with the same role for recommendation. In this method, each user is first represented by the same-role users who are co-occurring with the user. Then, similarities between users with the same role are measured based on obtained user representation. Finally, two variants of our method are proposed to fuse these computed similarities into traditional collaborative filtering for rating prediction. Using two publicly available real-world Epinions and Ciao datasets, we conduct comprehensive experiments to compare the performance of our proposed method with some existing benchmark methods. The results show that CF-TC outperforms other baseline methods in terms of RMSE, MAE, and recall.

• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.404-414
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• 2017

With the development of smartphones and online social networks, users produce a lot of contents and share them with each other. Therefore, users spend time by viewing or receiving the contents they do not want. In order to solve such problems, schemes for recommending useful contents have been actively studied. In this paper, we propose a contents recommendation scheme using collaborative filtering for users on online social networks. The proposed scheme consider a user trust in order to remove user data that lower the accuracy of recommendation. The user trust is derived by analyzing the user activity of online social network. For evaluating the user trust from various points of view, we collect user activities that have not been used in conventional techniques. It is shown through performance evaluation that the proposed scheme outperforms the existing scheme.