Human Decision Making and Recommender Systems

Jameson, Anthony; Willemsen, Martijn C.; Felfernig, Alexander; Gemmis, Marco de; Lops, Pasquale; Semeraro, Giovanni; Chen, Li

doi:10.1007/978-1-4899-7637-6_18

Cited by 74 publications

(36 citation statements)

References 62 publications

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“…Considering that users only spend a small amount of effort reading explanations, both content-based explanations are fairly effective in helping users make good decisions. However, apart from the content of items, many other factors may affect users' decision on whether to take recommendations, such as their trust in the system, social influence and past experience [12]. This is perhaps the reason why users perceive that natural language explanations contain richer information but are not significantly more effective in decision-making support.…”

Section: Discussionmentioning

confidence: 95%

See 1 more Smart Citation

Crowd-Based Personalized Natural Language Explanations for Recommendations

Chang

Harper

Terveen

2016

Proceedings of the 10th ACM Conference on Recommender Systems

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Explanations are important for users to make decisions on whether to take recommendations. However, algorithm generated explanations can be overly simplistic and unconvincing. We believe that humans can overcome these limitations. Inspired by how people explain word-of-mouth recommendations, we designed a process, combining crowdsourcing and computation, that generates personalized natural language explanations. We modeled key topical aspects of movies, asked crowdworkers to write explanations based on quotes from online movie reviews, and personalized the explanations presented to users based on their rating history. We evaluated the explanations by surveying 220 MovieLens users, finding that compared to personalized tagbased explanations, natural language explanations: 1) contain a more appropriate amount of information, 2) earn more trust from users, and 3) make users more satisfied. This paper contributes to the research literature by describing a scalable process for generating high quality and personalized natural language explanations, improving on state-of-the-art content-based explanations, and showing the feasibility and advantages of approaches that combine human wisdom with algorithmic processes.

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Section: Discussionmentioning

confidence: 95%

“…Our intuition is that by showing what users might like about recommended items (in a personalized fashion), we can convince them to explore or even consume unfamiliar items [12,15]. Our goal is to help users to decide whether or not to take a recommendation.…”

Section: Design Space and Related Workmentioning

confidence: 98%

Crowd-Based Personalized Natural Language Explanations for Recommendations

Chang

Harper

Terveen

2016

Proceedings of the 10th ACM Conference on Recommender Systems

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“…Failure to address explicitly these additional perspectives of the ethical impact of recommender systems may lead to masking seriously problematic practices. A case in point may be that of introducing a "bias" in favour of recommending unpopular items to maximise catalogue coverage in e-commerce applications (Jameson et al, 2015). This practice meets a specific need of the provider of a recommendation system, helping to minimise the number of unsold items, which in this specific instance may be considered a legitimate interest to be traded off against the utility that a user may receive from a more accurate recommendation.…”

Section: Resultsmentioning

confidence: 99%

“…Evaluating the system's performance as a decision support requires more elaborate metrics. For example, (Jameson et al, 2015) consider six strategies for generating recommendations, which track different choice patterns based on either of the following features: (1) the attributes of the options; (2) the expected consequences of choosing an option; (3) prior experience with similar options; (4) social pressure or social information about the options; (5) following a specific policy; (6) trial-and-error based choice.…”

Section: A Working Definition Of Recommender Systemsmentioning

confidence: 99%

Recommender Systems and their Ethical Challenges

2019

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This article presents the first, systematic analysis of the ethical challenges posed by recommender systems. Through a literature review, the article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders-as opposed to just the receivers of a recommendation-in assessing the ethical impacts of a recommender system.

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“…When it is, the personalization tends to focus on users' current behavior, thereby limiting rather than enhancing their ability to explore [33]. Since decisions are evaluations of possible future states [18], decision support-providing virtual agents should not only model the user's current state, but allow them to see what the possible future states could be, so as to be better able to cater to their long-term goals and desires [36].…”

Section: The Problemsmentioning

confidence: 99%

Human-Centric Preference Modeling for Virtual Agents

Knijnenburg

Hubig

2020

Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents

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Intelligent virtual agents increasingly use machine learning to model the needs and preferences of their users in order to give personalized support. In this paper we present a vision of łhumancentric modelingž that moves this practice beyond the automated estimation of users' current preferences based on observable behaviors to a modeling process that a) considers users' hidden characteristics rather than observable behaviors, b) models possible futures rather than users' current state, and c) gives users an opportunity to interactively control this modeling process. This vision focuses on the long-term needs of individual humans and a genuine betterment of society. CCS CONCEPTS • Information systems → Decision support systems; • Computing methodologies → Intelligent agents; • Human-centered computing → Human computer interaction (HCI).

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Human Decision Making and Recommender Systems

Cited by 74 publications

References 62 publications

Crowd-Based Personalized Natural Language Explanations for Recommendations

Crowd-Based Personalized Natural Language Explanations for Recommendations

Recommender Systems and their Ethical Challenges

Human-Centric Preference Modeling for Virtual Agents

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