Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs

By Weiquan Wang, Izak Benbasat. 2007

513 citations recorded by the source corpus.

Subfields: Information Systems and Management, Sociology and Political Science

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In Sociology and Political Science

Citations in the corpus

Explainable Recommendation: A Survey and New Perspectives (2018, background): “Similarly, Wu et al. [2017] combined the user-item interaction and review information in a unified framework.”
Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals (2016, background): “A related study finds that different types of explanations by a recommendation agent enhance users’ trust in the system.(18) Furthermore, there is evidence that when given the appropriate tools, people will choose to give up some of the benefits they derive from their social network activity to meet”
Explaining Recommendations: Design and Evaluation (2015, background): “myCameraAdvisor [82] e.”
The effects of transparency on trust in and acceptance of a content-based art recommender (2008, methodology): “Wang and Benbasat (2007) compared the effects of three types of explanations on user trust in an e-commerce recommender.”
Examining Trust in Information Technology Artifacts: The Effects of System Quality and Culture (2008, background): “attributes such as integrity, benevolence, and trust [86, 87], users are forced to form trusting beliefs based on attributes of the m-commerce portal itself [50, 57].”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “The two most common questions are why and how the model under scrutiny produces its predictions/inferences [102, 103, 2, 7].”
Evaluating Anthropomorphic Product Recommendation Agents: A Social Relationship Perspective to Designing Information Systems (2009, methodology): “the PRa designed for the experiment was a recommendation system for digital cameras, which was adapted and extended from earlier works by Wang and Benbasat [124, 125].”
Designing and Evaluating Explanations for Recommender Systems (2011, background): “Also [63] query users about trust, but focus on trust related beliefs such as the perceived competence, benevolence and integrity of a virtual adviser.”
Empirical Analysis of the Impact of Recommender Systems on Sales (2010, background): “however, customers may not trust recommendations whose rationale is not properly explained [47].”
Crowd-Based Personalized Natural Language Explanations for Recommendations (2016, background): “When designing recommendation explanations, there are several “styles” [21] of explanations to choose from: casebased, collaborative-based [11], content-based [22], conversational [19], demographic-based, and knowledge-based [24].”
Why I like it: multi-task learning for recommendation and explanation (2018, background): “The traditional approach to recommendation explanation has an important limitation since it can only learn user preferences and item properties that are explicitly stated in observed reviews [44].”