Great Explanations: Opinionated Explanations for Recommendations

Muhammad, Khalil; Lawlor, Aonghus; Rafter, Rachael; Smyth, Barry

doi:10.1007/978-3-319-24586-7_17

Cited by 27 publications

(13 citation statements)

References 17 publications

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“…Approach [166,34,56,206,2,124,54,146,198,192,129,131,86,138,82,136,24,97,4,171,249,84,231,94,101,143,252,149,85,208,77,189,220,68,64,6,52,29,167,230,23,96,47,242,117,254,15,46,9,156,44,236,88,…”

Section: Selection Of Primary Studiesmentioning

confidence: 99%

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A systematic review and taxonomy of explanations in decision support and recommender systems

Nunes

Jannach

2017

User Model User-Adap Inter

279

193

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With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems.A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advicegiving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.

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Section: Selection Of Primary Studiesmentioning

confidence: 99%

“…As discussed, some explanations provide information associated with background knowledge, but such knowledge is almost always associated with the decision inference process. Four approaches [254,46,156,44] in the e-commerce domain exploited external sources of information, namely product reviews.…”

Section: External Sources Of Explanation Contentmentioning

confidence: 99%

A systematic review and taxonomy of explanations in decision support and recommender systems

Nunes

Jannach

2017

User Model User-Adap Inter

279

193

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“…There are a number of different approaches and studies that deal with recommendations and which bring new methods in the area of personalization models or explanations [16,24]. But especially explanations are still quite new areas of actual research.…”

Section: Real-world Explanations Applicationsmentioning

confidence: 99%

Towards understandable personalized recommendations: Hybrid explanations

2019

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Nowadays, personalized recommendations are widely used and popular. There are a lot of systems in various fields, which use recommendations for different purposes. One of the basic problems is the distrust of users of recommended systems. Users often consider the recommendations as an intrusion of their privacy. Therefore, it is important to make recommendations transparent and understandable to users. To address these problems, we propose a novel hybrid method of personalized explanation of recommendations. Our method is independent of recommendation technique and combines basic explanation styles to provide the appropriate type of personalized explanation to each user. We conducted several online experiments in the news domain. Obtained results clearly show that the proposed personalized hybrid explanation approach improves the users' attitude towards the recommender, moreover, we have observed the increase of recommendation precision.

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“…Another focus is identifying the sets of aspects with higher positive/negative polarity to give insights into the reason why items have been chosen [8]. Those approaches need previously to group the aspects to reduce the granularity in order to provide useful recommendations, often solved by clustering aspects using background knowledge to simplify the process.…”

Section: Related Workmentioning

confidence: 99%

Concept Discovery and Argument Bundles in the Experience Web

Ferrer

Plaza

2016

Case-Based Reasoning Research and Development

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Abstract. In this paper we focus on a particular interesting web usergenerated content: people's experiences. We extend our previous work on aspect extraction and sentiment analysis and propose a novel approach to create a vocabulary of basic level concepts with the appropriate granularity to characterize a set of products. This concept vocabulary is created by analyzing the usage of the aspects over a set of reviews, and allows us to find those features with a clear positive and negative polarity to create the bundles of arguments. The argument bundles allow us to define a concept-wise satisfaction degree of a user query over a set of bundles using the notion of fuzzy implication, allowing the reuse experiences of other people to the needs a specific user.

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Great Explanations: Opinionated Explanations for Recommendations

Cited by 27 publications

References 17 publications

A systematic review and taxonomy of explanations in decision support and recommender systems

A systematic review and taxonomy of explanations in decision support and recommender systems

Towards understandable personalized recommendations: Hybrid explanations

Concept Discovery and Argument Bundles in the Experience Web

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