Interfaces for Eliciting New User Preferences in Recommender Systems

McNee, Sean M.; Lam, Shyong K.; Konstan, Joseph A.; Riedl, John

doi:10.1007/3-540-44963-9_24

Cited by 100 publications

(68 citation statements)

References 9 publications

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“…McNee et al (2003) found that adding a confidence metric to a movie recommender, indicating which recommendations were 'more risky', increased user satisfaction and influenced user behaviour. However, they also found that more experienced users of the movie recommender were less satisfied with the system after being instructed about the confidence rating.…”

Section: Transparency Of User-adaptive Systems and Study Hypothesesmentioning

confidence: 99%

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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: Transparency Of User-adaptive Systems and Study Hypothesesmentioning

confidence: 99%

“…More concrete data might be just as, or more, powerful in risk assessment and their effect on trust. McNee et al (2003) have shown that simple certainty ratings can indeed invoke more appropriate usage decisions.…”

Section: Effects Of Certainty Ratings On Interactionmentioning

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

“…Thus, higher level of effort necessitates more time to accomplish a given task (Hostler et al, 2005). Indeed, many studies use decision time as an indicator of decision effort (Hostler et al, 2005;McNee, Lam, Konstan, & Riedl, 2003;Olson & Widing, 2002). Further, the level of effort required to make a decision is strongly related to the amount of information that needs to be considered (Eppler & Mengis, 2004), and so the average number of alternatives in a result set can be considered a good indicator of decision effort.…”

Section: Decision Effortmentioning

confidence: 99%

The performance of recommender systems in online shopping: A user-centric study

Dąbrowski

Acton

2013

Expert Systems with Applications

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Abstract. This research investigates the effects of preference relaxation on decisionmaking performance of users in online preference-based product search contexts. We compare four recommender systems based on different preference relaxation methods in extensive user experiments with 111 subjects that use two real-world datasets: 1818 digital cameras and 45278 used car advertisements gathered from popular e-commerce websites. Our results provide new insights into the positive impact of the soft-boundary preference relaxation methods on decision-making quality and effort. The paper extends previous studies on this topic and demonstrates that decision aids based on preference relaxation techniques can effectively enhance preference-based product search in online product catalogues and help alleviate common disadvantages of form-based filtering mechanisms.

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“…For example, in [10] it has been shown that in a collaborative filtering system, letting users themselves propose items they want to rate yields better results than a strategy where the items are chosen to optimally elicit the preference model.…”

Section: Introductionmentioning

confidence: 99%

The Lookahead Principle for Preference Elicitation: Experimental Results

Viappiani¹,

Faltings²,

2006

Flexible Query Answering Systems

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Abstract. Preference-based search is the problem of finding an item that matches best with a user's preferences. User studies show that example-based tools for preference-based search can achieve significantly higher accuracy when they are complemented with suggestions chosen to inform users about the available choices. We discuss the problem of eliciting preferences in example-based tools and present the lookahead principle for generating suggestions. We compare two different implementations of this principle and we analyze logs of real user interactions to evaluate them.

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Interfaces for Eliciting New User Preferences in Recommender Systems

Cited by 100 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

The performance of recommender systems in online shopping: A user-centric study

The Lookahead Principle for Preference Elicitation: Experimental Results

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