A survey on effects of adding explanations to recommender systems

Vultureanu‐Albişi, Alexandra; Bǎdicǎ, Costin

doi:10.1002/cpe.6834

Cited by 14 publications

(3 citation statements)

References 47 publications

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“…In recent years, researchers have tried to make the results of these "blackbox" architectures more understandable to human subjects. Surveys from [10], [325] comprehensively cover the latest efforts and emphasize the desirability of robust RSs that can be perceived as reliable and transparent by the users. Some works focus on explicitly modeling latent factors or user profiles [326] and propose the usage of template-based systems to generate user data [253], [327].…”

Section: B Explainable Recommender Systemsmentioning

confidence: 99%

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

et al. 2022

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Research on recommendation systems is swiftly producing an abundance of novel methods, constantly challenging the current state-of-the-art. Inspired by advancements in many related fields, like Natural Language Processing and Computer Vision, many hybrid approaches based on deep learning are being proposed, making solid improvements over traditional methods. On the downside, this flurry of research activity, often focused on improving over a small number of baselines, makes it hard to identify reference methods and standardized evaluation protocols. Furthermore, the traditional categorization of recommendation systems into content-based, collaborative filtering and hybrid systems lacks the informativeness it once had. With this work, we provide a gentle introduction to recommendation systems, describing the task they are designed to solve and the challenges faced in research. Building on previous work, an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information. To ease the approach toward the technical methodologies recently proposed in this field, we review several representative methods selected primarily from top conferences and systematically describe their goals and novelty. We formalize the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks. Lastly, we discuss issues in current research practices by analyzing experimental results reported on three popular datasets.

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Section: B Explainable Recommender Systemsmentioning

confidence: 99%

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

et al. 2022

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“…Explainability of Recommender Systems (RS) is an important field which studies methods that learn why a recommendation is suggested by a model for a user [14,15,39]. The explanations provided improve the transparency of the system, by revealing more about the predicted outcome as in how does the model learn personalized preferences for every user [37,45]. Moreover, explanations provided within a RS framework can directly appeal to a user and even influence them to purchase an item if it is very well explained (by providing very detailed information associated with the recommendation) as to why it is recommended to the user [26].…”

Section: Introductionmentioning

confidence: 99%

Stability of Explainable Recommendation

Vijayaraghavan,

Mohapatra

2023

Proceedings of the 17th ACM Conference on Recommender Systems

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Explainable Recommendation has been gaining attention over the last few years in industry and academia. Explanations provided along with recommendations in a recommender system framework have many uses: particularly reasoning why a suggestion is provided and how well an item aligns with a user's personalized preferences. Hence, explanations can play a huge role in influencing users to purchase products. However, the reliability of the explanations under varying scenarios has not been strictly verified from an empirical perspective. Unreliable explanations can bear strong consequences such as attackers leveraging explanations for manipulating and tempting users to purchase target items that the attackers would want to promote. In this paper, we study the vulnerability of existent feature-oriented explainable recommenders, particularly analyzing their performance under different levels of external noises added into model parameters. We conducted experiments by analyzing three important state-of-the-art (SOTA) explainable recommenders when trained on two widely used e-commerce based recommendation datasets of different scales. We observe that all the explainable models are vulnerable to increased noise levels. Experimental results verify our hypothesis that the ability to explain recommendations does decrease along with increasing noise levels and particularly adversarial noise does contribute to a much stronger decrease. Our study presents an empirical verification on the topic of robust explanations in recommender systems which can be extended to different types of explainable recommenders in RS.

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“…As a result, the need to boost people's trust in such systems by allowing them to explain their decision‐making processes in human‐understandable terms via explanations arose. The authors give a survey on the effects of explainable artificial intelligence (XAI) in recommender systems, and a structured presentation of the evaluation of recommender systems in order to comprehend intelligent human evaluation systems and a process for evaluating human‐centered explanations 2 …”

mentioning

confidence: 99%