Gradual trust and distrust in recommender systems

Victor, Patricia; Cornelis, Chris; Cock, Martine De; Silva, Paulo Pinheiro da

doi:10.1016/j.fss.2008.11.014

Cited by 176 publications

(124 citation statements)

References 9 publications

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“…In such settings trust in the online vendor's goals in providing recommendations might also play a role. In collaborative-based recommenders, users need to assess whether they trust the intentions and actions of other users (Victor et al 2008). In addition, in contexts where a system might provide information about a recommended item not immediately verifiable by users, trust in the correctness and credibility of the information might play a role as well (Fogg and Tseng 2003).…”

Section: Interaction With User-adaptive Systemsmentioning

confidence: 99%

The effects of transparency on trust in and acceptance of a content-based art recommender

Cramer

Evers

Ramlal

et al. 2008

User Model User-Adap Inter

340

207

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The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment (N = 60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study's results.

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Section: Interaction With User-adaptive Systemsmentioning

confidence: 99%

The effects of transparency on trust in and acceptance of a content-based art recommender

Cramer

Evers

Ramlal

et al. 2008

User Model User-Adap Inter

340

207

View full text Add to dashboard Cite

show abstract

“…Therefore, it could also be argued its inability to model appropriately vague statements that are assessed using the concepts of 'trust' and 'distrust'. The concept of trust function has been regarded in [34,35] as a reliable tool to deal with agents' vague by trust degree and distrust degree. Considering that multiple experts might have fuzzier and more uncertainty opinions about alternatives as previously said, this article aims to investigate an interval-valued trust score space in which the trust degree and distrust degree are expressed by interval-valued numbers rather than trip values as FSs allow to.…”

Section: Interval-valued Trust Decision Making Spacementioning

confidence: 99%

A trust induced recommendation mechanism for reaching consensus in group decision making

Liu

Liang

Chiclana

et al. 2017

Knowledge-Based Systems

160

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“…User Trust can be computed through explicit information (e.g., trust networks [14], [26] distrust analysis [27], personality based similarity measure [4] etc.) or through implicit information obtained in the form of social network (e.g., trust propagation mechanism [5]).…”

Section: Social Recommender Systemmentioning

confidence: 99%

Systematic Evaluation of Social Recommendation Systems: Challenges and Future

Rastogi¹,

Singh²

2016

ijacsa

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Abstract-The issue of information overload could be effectively managed with the help of intelligent system which is capable of proactively supervising the users in accessing relevant or useful information in a tailored way, by pruning the large space of possible options. But the key challenge lies in what all information can be collected and assimilated to make effective recommendations. This paper discusses reasons for evolution of recommender systems leading to transition from traditional to social information based recommendations. Social Recommender System (SRS) exploits social contextual information in the form of social links of users, social tags, user-generated data that contain huge supplemental information about items or services that are expected to be of interest of user or about features of items. Therefore, having tremendous potential for improving recommendation quality. Systematic literature review has been done for SRS by categorizing various kinds of social-contextual information into explicit and implicit user-item information. This paper also analyses key aspects of any generic recommender system namely Domain, Personalization Levels, Privacy and Trustworthiness, Recommender algorithms to give a better understanding to researchers new in this field.

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Gradual trust and distrust in recommender systems

Cited by 176 publications

References 9 publications

The effects of transparency on trust in and acceptance of a content-based art recommender

The effects of transparency on trust in and acceptance of a content-based art recommender

A trust induced recommendation mechanism for reaching consensus in group decision making

Systematic Evaluation of Social Recommendation Systems: Challenges and Future

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