Explanation in Recommender Systems

By David McSherry. 2005

216 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “If our recommendation explanation is automatically generated as a piece of free text, we can take frequently used text generation measures for evaluation, such as BLEU (bilingual evaluation understudy) score (Papineni et al. [2002]) and ROUGE (recall-oriented understudy for gisting evaluation) score”
A Survey of Methods for Explaining Black Box Models (2018, background): “arch question is related to providing explanations in the eld of recommender systems. When a suggestion is provided to a customer, it should come together with the reasons for this recommendation. In [67] the authors dene a case-based reasoning approach to generate recommendations with the opportuni”
Trust building with explanation interfaces (2006, background): “Case-based reasoning recommender systems that can explain their recommendations include ExpertClerk [21], Dynamic critiquing systems [10], FirstCase and TopCase [14, 15].”
Retrieval, reuse, revision and retention in case-based reasoning (2005, unknown): “For example, McSherry (2003a; 2005) proposes a conversational CBR approach to product recommendation in which the system can explain why a question is being asked in terms of its ability to discriminate between competing cases.”
A systematic review and taxonomy of explanations in decision support and recommender systems (2017, unknown): “f our analysis, can be found online at: http://inf.ufrgs.br/prosoft/resources/2017/umuai-sr-explanations. A Systematic Review and Taxonomy of Explanations 9 Table 6 Selected Primary Studies. Approach [166,34,56,206,2,124,54,146,198,192,129,131,86,138,82,136,24,97,4,171,249,84,231,94,101, 143,252,149”
Explanations of recommendations (2007, background): “This has been done by helping the user understand the relation between competing options [13, 14].”
Aspects of Intelligent Systems Explanation (2013, methodology): “For example, [46] uses a CBR system called ProCon, which adapts using Bayesian inference.”
The effects of transparency on trust in and acceptance of a content-based art recommender (2008, background): “McSherry (2005) discusses an approach to finding a fitting product recommendation by guiding the user through the features of the available products, which can for example help understanding the trade-offs and interdependencies between product features.”
A Survey of Explanations in Recommender Systems (2007, background): “Top Case [24] Holiday Top-item, Similar to top item Preference-based Specify reqs.”
Knowledgeable Explanations for Recommender Systems (2010, background): “In contrast McSherry [9] evaluated case-based explanations that provide arguments of the form: " Case X differs from your query only in attribute A and is the best case no matter what you prefer for attribute B. " The latter comes already closer to the form of explanations presented in this paper, w”
SAsSy - Making Decisions Transparent with Argumentation and Natural Language Generation (2014, background): “Explanations have frequently been a component of intelligent systems (IS) such as expert systems [1, 4, 9] and recommender systems [6, 8].”
Explanation in Case-Based Reasoning–Perspectives and Goals (2005, background): “The Top Case mixed-initiative recommender system pursues a strategy where it selects questions that potentially strengthen the match for its currently selected best hypothesis case (McSherry, 2005).”
Transparent, Scrutable and Explainable User Models for Personalized Recommendation (2019, background): “Various forms of explanations have been explored in prior work, including sentences [21, 55], tag/keyword clouds [17, 30, 53], as well as different kinds of visualizations [10, 22, 31, 47].”
Explaining Recommendations: Design and Evaluation (2015, background): “Top Case [50] “Case 574 differs from your query only in price and is the best case no matter what transport, duration, or accommodation you prefer” Knowledge/utility-based”
The Effectiveness of Personalized Movie Explanations: An Experiment Using Commercial Meta-data (2008, background): “Our work focuses on explanations of recommended items [1,2,3], explaining how a user might relate to an item unknown to them.”
Evaluating the effectiveness of explanations for recommender systems (2012, background): “Since then, the body of research on explanations in recommender systems has continued to grow (e.g., Bilgic and Mooney 2005; McCarthy et al. 2005b; McSherry 2005; Pu and Chen 2007; Cramer et al. 2008a; Tintarev and Masthoff 2008b; Guy et al. 2009a; Vig et al. 2009; Tintarev and Masthoff 2010).”