Explaining Recommendations Using Contexts

Sato, Masahiro; Ahsan, Budrul; Nagatani, Koki; Sonoda, Takashi; Zhang, Qian; Ohkuma, Tomoko

doi:10.1145/3172944.3173012

Cited by 18 publications

(21 citation statements)

References 23 publications

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“…In our work, we do not take context into account-we leave this to our future work. Another difference is that the system of Sato et al [37] compared seven different explanation styles, but these explanation styles could not be combined together to form one hybrid explanation as in our case. The highest number of hybrid explanations that could be provided was three, while in our case the total number is seven.…”

Section: Related Workmentioning

confidence: 85%

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Generating and Understanding Personalized Explanations in Hybrid Recommender Systems

Kouki¹,

Schaffer

Pujara

et al. 2020

ACM Trans. Interact. Intell. Syst.

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Recommender systems are ubiquitous and shape the way users access information and make decisions. As these systems become more complex, there is a growing need for transparency and interpretability. In this article, we study the problem of generating and visualizing personalized explanations for recommender systems that incorporate signals from many different data sources. We use a flexible, extendable probabilistic programming approach and show how we can generate real-time personalized recommendations. We then turn these personalized recommendations into explanations. We perform an extensive user study to evaluate the benefits of explanations for hybrid recommender systems. We conduct a crowd-sourced user study where our system generates personalized recommendations and explanations for real users of the last.fm music platform. First, we evaluate the performance of the recommendations in terms of perceived accuracy and novelty. Next, we experiment with (1) different explanation styles (e.g., user-based, item-based), (2) manipulating the number of explanation styles presented, and (3) manipulating the presentation format (e.g., textual vs. visual). We also apply a mixed-model statistical analysis to consider user personality traits as a control variable and demonstrate the usefulness of our approach in creating personalized hybrid explanations with different style, number, and format. Finally, we perform a post analysis that shows different preferences for explanation styles between experienced and novice last.fm users.

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Section: Related Workmentioning

confidence: 85%

“…Sato et al [37] proposed to use contexts for explanations (e.g., usage scenarios or accompanying persons). The proposed recommender system needed to select the most relevant pairs of contexts and items for the users.…”

Section: Related Workmentioning

confidence: 99%

Generating and Understanding Personalized Explanations in Hybrid Recommender Systems

Kouki¹,

Schaffer

Pujara

et al. 2020

ACM Trans. Interact. Intell. Syst.

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“…The main goal is to inform the user decides whether to accept the recommendation. This method used in the literature to foster trust [27], transparency [27], persuasiveness [72], user acceptance [39] and prevent errors and bias [73]. The cost-benefit analysis is challenging design consideration [47,11], as users may perceive the cost of reading explanations to exceed their benefits [11].…”

Section: Delivery Methodsmentioning

confidence: 99%

Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks

Naiseh

Jiang

et al. 2020

Research Challenges in Information Science

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With the increase in data volume, velocity and types, intelligent human-agent systems have become popular and adopted in different application domains, including critical and sensitive areas such as health and security. Humans' trust, their consent and receptiveness to recommendations are the main requirement for the success of such services. Recently, the demand on explaining the recommendations to humans has increased both from humans interacting with these systems so that they make an informed decision and, also, owners and systems managers to increase transparency and consequently trust and users' retention. Existing systematic reviews in the area of explainable recommendations focused on the goal of providing explanations, their presentation and informational content. In this paper, we review the literature with a focus on two user experience facets of explanations; delivery methods and modalities. We then focus on the risks of explanation both on user experience and their decision making. Our review revealed that explanations delivery to end-users is mostly designed to be along with the recommendation in a push and pull styles while archiving explanations for later accountability and traceability is still limited. We also found that the emphasis was mainly on the benefits of recommendations while risks and potential concerns, such as over-reliance on machines, is still a new area to explore.

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“…Subjective perceptions of explanations are often evaluated qualitatively based on user surveys, with responses typically given on Likert scales [6,8,10,17,[21][22][23]. Following standard practice, we design a user survey to capture the subjective perception of users regarding the seven goals.…”

Section: Evaluating Explanationsmentioning

confidence: 99%

Measuring Recommendation Explanation Quality

Balog

Radlinski

2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Explanations have a large effect on how people respond to recommendations. However, there are many possible intentions a system may have in generating explanations for a given recommendationfrom increasing transparency, to enabling a faster decision, to persuading the recipient. As a good explanation for one goal may not be good for others, we address the questions of (1) how to robustly measure if an explanation meets a given goal and (2) how the different goals interact with each other. Specifically, this paper presents a first proposal of how to measure the quality of explanations along seven common goal dimensions catalogued in the literature. We find that the seven goals are not independent, but rather exhibit strong structure. Proposing two novel explanation evaluation designs, we identify challenges in evaluation, and provide more efficient measurement approaches of explanation quality. CCS CONCEPTS • Information systems → Recommender systems; Presentation of retrieval results; • Human-centered computing → Natural language interfaces; HCI design and evaluation methods.

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Explaining Recommendations Using Contexts

Cited by 18 publications

References 23 publications

Generating and Understanding Personalized Explanations in Hybrid Recommender Systems

Generating and Understanding Personalized Explanations in Hybrid Recommender Systems

Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks

Measuring Recommendation Explanation Quality

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