@article{
 title = {Trust-inspiring explanation interfaces for recommender systems},
 type = {article},
 year = {2007},
 identifiers = {[object Object]},
 keywords = {Competence perception,Decision support,Explanation interfaces,Interface design,Recommender agents,Recommender systems,Trust model,Trusting intentions,User evaluation},
 pages = {542-556},
 volume = {20},
 id = {76c5d760-80ae-3108-805c-4a88fb7b3a8b},
 created = {2018-03-17T17:53:05.027Z},
 file_attached = {false},
 profile_id = {2ed0fe69-06a2-3e8b-9bc9-5bdb197f1120},
 group_id = {e795dbfa-5576-3499-9c01-6574f19bf7aa},
 last_modified = {2018-12-14T12:16:32.608Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Pu2007},
 notes = {La calidad de las recomendaciones que se hace a los usuarios está relacionada con la intención de volver a usar la plataforma, aunque no necesariamente con la adquisición de los productos que se recomiendan o se venden en la plataforma. Un ejemplo de explicación que aporta confianza al usuario es la de la interfaz "organizada" porque permite al usuario hacer comparaciones entre los diferentes productos disponibles y a tomar mejores decisiones a la hora de escoger qué producto es el que más le conviene. Se obtiene mejor resultado que con la interfaz "why?", donde los usuarios no pueden observar con tanta claridad (transparencia) por qué se recomiendan esos productos. (Las interfaces se muestran en las figuras 3 y 4 en el artículo).},
 private_publication = {false},
 abstract = {A recommender system's ability to establish trust with users and convince them of its recommendations, such as which camera or PC to purchase, is a crucial design factor especially for e-commerce environments. This observation led us to build a trust model for recommender agents with a focus on the agent's trustworthiness as derived from the user's perception of its competence and especially its ability to explain the recommended results. We present in this article new results of our work in developing design principles and algorithms for constructing explanation interfaces. We show the effectiveness of these principles via a significant-scale user study in which we compared an interface developed based on these principles with a traditional one. The new interface, called the organization interface where results are grouped according to their tradeoff properties, is shown to be significantly more effective in building user trust than the traditional approach. Users perceive it more capable and efficient in assisting them to make decisions, and they are more likely to return to the interface. We therefore recommend designers to build trust-inspiring interfaces due to their high likelihood to increase users' intention to save cognitive effort and the intention to return to the recommender system. © 2007 Elsevier B.V. All rights reserved.},
 bibtype = {article},
 author = {Pu, Pearl and Chen, Li},
 journal = {Knowledge-Based Systems},
 number = {6}
}

{"_id":"43KadWanRz3dQeEQf","bibbaseid":"pu-chen-trustinspiringexplanationinterfacesforrecommendersystems-2007","downloads":0,"creationDate":"2018-09-05T13:52:59.085Z","title":"Trust-inspiring explanation interfaces for recommender systems","author_short":["Pu, P.","Chen, L."],"year":2007,"bibtype":"article","biburl":null,"bibdata":{"title":"Trust-inspiring explanation interfaces for recommender systems","type":"article","year":"2007","identifiers":"[object Object]","keywords":"Competence perception,Decision support,Explanation interfaces,Interface design,Recommender agents,Recommender systems,Trust model,Trusting intentions,User evaluation","pages":"542-556","volume":"20","id":"76c5d760-80ae-3108-805c-4a88fb7b3a8b","created":"2018-03-17T17:53:05.027Z","file_attached":false,"profile_id":"2ed0fe69-06a2-3e8b-9bc9-5bdb197f1120","group_id":"e795dbfa-5576-3499-9c01-6574f19bf7aa","last_modified":"2018-12-14T12:16:32.608Z","read":"true","starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Pu2007","notes":"La calidad de las recomendaciones que se hace a los usuarios está relacionada con la intención de volver a usar la plataforma, aunque no necesariamente con la adquisición de los productos que se recomiendan o se venden en la plataforma. Un ejemplo de explicación que aporta confianza al usuario es la de la interfaz "organizada" porque permite al usuario hacer comparaciones entre los diferentes productos disponibles y a tomar mejores decisiones a la hora de escoger qué producto es el que más le conviene. Se obtiene mejor resultado que con la interfaz "why?", donde los usuarios no pueden observar con tanta claridad (transparencia) por qué se recomiendan esos productos. (Las interfaces se muestran en las figuras 3 y 4 en el artículo).","private_publication":false,"abstract":"A recommender system's ability to establish trust with users and convince them of its recommendations, such as which camera or PC to purchase, is a crucial design factor especially for e-commerce environments. This observation led us to build a trust model for recommender agents with a focus on the agent's trustworthiness as derived from the user's perception of its competence and especially its ability to explain the recommended results. We present in this article new results of our work in developing design principles and algorithms for constructing explanation interfaces. We show the effectiveness of these principles via a significant-scale user study in which we compared an interface developed based on these principles with a traditional one. The new interface, called the organization interface where results are grouped according to their tradeoff properties, is shown to be significantly more effective in building user trust than the traditional approach. Users perceive it more capable and efficient in assisting them to make decisions, and they are more likely to return to the interface. We therefore recommend designers to build trust-inspiring interfaces due to their high likelihood to increase users' intention to save cognitive effort and the intention to return to the recommender system. © 2007 Elsevier B.V. All rights reserved.","bibtype":"article","author":"Pu, Pearl and Chen, Li","journal":"Knowledge-Based Systems","number":"6","bibtex":"@article{\n title = {Trust-inspiring explanation interfaces for recommender systems},\n type = {article},\n year = {2007},\n identifiers = {[object Object]},\n keywords = {Competence perception,Decision support,Explanation interfaces,Interface design,Recommender agents,Recommender systems,Trust model,Trusting intentions,User evaluation},\n pages = {542-556},\n volume = {20},\n id = {76c5d760-80ae-3108-805c-4a88fb7b3a8b},\n created = {2018-03-17T17:53:05.027Z},\n file_attached = {false},\n profile_id = {2ed0fe69-06a2-3e8b-9bc9-5bdb197f1120},\n group_id = {e795dbfa-5576-3499-9c01-6574f19bf7aa},\n last_modified = {2018-12-14T12:16:32.608Z},\n read = {true},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Pu2007},\n notes = {La calidad de las recomendaciones que se hace a los usuarios está relacionada con la intención de volver a usar la plataforma, aunque no necesariamente con la adquisición de los productos que se recomiendan o se venden en la plataforma. Un ejemplo de explicación que aporta confianza al usuario es la de la interfaz "organizada" porque permite al usuario hacer comparaciones entre los diferentes productos disponibles y a tomar mejores decisiones a la hora de escoger qué producto es el que más le conviene. Se obtiene mejor resultado que con la interfaz "why?", donde los usuarios no pueden observar con tanta claridad (transparencia) por qué se recomiendan esos productos. (Las interfaces se muestran en las figuras 3 y 4 en el artículo).},\n private_publication = {false},\n abstract = {A recommender system's ability to establish trust with users and convince them of its recommendations, such as which camera or PC to purchase, is a crucial design factor especially for e-commerce environments. This observation led us to build a trust model for recommender agents with a focus on the agent's trustworthiness as derived from the user's perception of its competence and especially its ability to explain the recommended results. We present in this article new results of our work in developing design principles and algorithms for constructing explanation interfaces. We show the effectiveness of these principles via a significant-scale user study in which we compared an interface developed based on these principles with a traditional one. The new interface, called the organization interface where results are grouped according to their tradeoff properties, is shown to be significantly more effective in building user trust than the traditional approach. Users perceive it more capable and efficient in assisting them to make decisions, and they are more likely to return to the interface. We therefore recommend designers to build trust-inspiring interfaces due to their high likelihood to increase users' intention to save cognitive effort and the intention to return to the recommender system. © 2007 Elsevier B.V. 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