Scrutable User Models and Personalised Item Recommendation in Mobile Lifestyle Applications

Wasinger, Rainer; Wallbank, J.; Pizzato, Luiz; Kay, Judy; Kummerfeld, Bob; Böhmer, Matthias; Krüger, Antonio

doi:10.1007/978-3-642-38844-6_7

Cited by 19 publications

(14 citation statements)

References 11 publications

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“…Explainability is the ability of an algorithm to be interpretable [27]. In holistic recommendation, a way to achieve explainability consists in enabling the user to visualize her models following a scrutable approach [53]. Open issues here relate to how to make such a complex User Model scrutable.…”

Section: Discussionmentioning

confidence: 99%

Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

et al. 2020

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Recommender systems (RSs) are systems that produce individualized recommendations as output or drive the user in a personalized way to interesting or useful objects in a space of possible options. Recently, RSs emerged as an effective support for decision making. However, when people make decisions, they usually take into account different and often conflicting information such as preferences, long-term goals, context, and their current condition. This complexity is often ignored by RSs. In order to provide an effective decision-making support, a RS should be "holistic", i.e., it should rely on a complete representation of the user, encoding heterogeneous user features (such as personal interests, psychological traits, health data, social connections) that may come from multiple data sources. However, to obtain such holistic recommendations some steps are necessary: first, we need to identify the goal of the decision-making process; then, we have to exploit common-sense and domain knowledge to provide the user with the most suitable suggestions that best fit the recommendation scenario. In this article, we present a methodological framework that can drive researchers and developers during the design process of this kind of "holistic" RS. We also provide evidence of the framework validity by presenting the design process and the evaluation of a food RS based on holistic principles.

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Section: Discussionmentioning

confidence: 99%

Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

et al. 2020

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“…Later in [34], a more complicated hybrid music recommendation system is developed, where users can move sliders associated with the weights assigned on various items or features to observe the changes on the recommendation list. Other works combine scrutability with profile setting [164,289]. In [289], a scrutable recommender system is used to recommend meals at a particular restaurant, where users are notified about how the preference contributes to the meal's personalization score.…”

Section: Explicit Controllabilitymentioning

confidence: 99%

A Survey on Trustworthy Recommender Systems

Ge¹,

Liu²,

Fu³

et al. 2022

Preprint

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Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead to undesired counter-effects on users, items, producers, platforms, or even the society at large, such as compromised user trust due to non-transparency, unfair treatment of different consumers, or producers, privacy concerns due to extensive use of user's private data for personalization, just to name a few. All of these create an urgent need for Trustworthy Recommender Systems (TRS) so as to mitigate or avoid such adverse impacts and risks. In this survey, we will introduce techniques related to trustworthy and responsible recommendation, including but not limited to explainable recommendation, fairness in recommendation, privacy-aware recommendation, robustness in recommendation, user controllable recommendation, as well as the relationship between these different perspectives in terms of trustworthy and responsible recommendation. Through this survey, we hope to deliver readers with a comprehensive view of the research area and raise attention to the community about the importance, existing research achievements, and future research directions on trustworthy recommendation.

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“…This strategy can be found, for instance, in a meal recommendation system by Wasinger et al [49]: Users receive a list of meals, recommended based on their personal eating preferences. The interface prominently includes a button that allows users to enable or disable this recommendation feature.…”

Section: Allow Users To Turn Intelligence On and Offmentioning

confidence: 99%

A Method and Analysis to Elicit User-reported Problems in Intelligent Everyday Applications

Eiband¹,

Völkel²,

Buschek³

et al. 2020

Preprint

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The complex nature of intelligent systems motivates work on supporting users during interaction, for example through explanations. However, as of yet, there is little empirical evidence in regard to specific problems users face when applying such systems in everyday situations. This paper contributes a novel method and analysis to investigate such problems as reported by users: We analysed 45,448 reviews of four apps on the Google Play Store (Facebook, Netflix, Google Maps and Google Assistant) with sentiment analysis and topic modelling to reveal problems during interaction that can be attributed to the apps' algorithmic decision-making. We enriched this data with users' coping and support strategies through a follow-up online survey (N=286). In particular, we found problems and strategies related to content, algorithm, user choice, and feedback. We discuss corresponding implications for designing user support, highlighting the importance of user control and explanations of output, rather than processes.Author pre-print, to appear in Special Issue on Highlights of ACM Intelligent User Interface (IUI) 2019. ACM Trans. Interact. Intell. Syst.

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Scrutable User Models and Personalised Item Recommendation in Mobile Lifestyle Applications

Cited by 19 publications

References 11 publications

Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

A Survey on Trustworthy Recommender Systems

A Method and Analysis to Elicit User-reported Problems in Intelligent Everyday Applications

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