An approach to controlling user models and personalization effects in recommender systems

Bakalov, Fedor; Meurs, Marie-Jean; König‐Ries, Birgitta; Sateli, Bahar; Witte, René; Butler, Gregory; Tsang, Adrian

doi:10.1145/2449396.2449405

Cited by 41 publications

(34 citation statements)

References 15 publications

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“…This has motivated researchers to begin measuring the personalization present in deployed systems, such as web search engines [17,27,50] and recommender systems [4].…”

Section: The Filter Bubblementioning

confidence: 99%

Measuring Price Discrimination and Steering on E-commerce Web Sites

Hannák

Soeller

Lazer

et al. 2014

Proceedings of the 2014 Conference on Internet Measurement Conference

227

144

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Today, many e-commerce websites personalize their content, including Netflix (movie recommendations), Amazon (product suggestions), and Yelp (business reviews). In many cases, personalization provides advantages for users: for example, when a user searches for an ambiguous query such as "router," Amazon may be able to suggest the woodworking tool instead of the networking device. However, personalization on e-commerce sites may also be used to the user's disadvantage by manipulating the products shown (price steering) or by customizing the prices of products (price discrimination). Unfortunately, today, we lack the tools and techniques necessary to be able to detect such behavior.In this paper, we make three contributions towards addressing this problem. First, we develop a methodology for accurately measuring when price steering and discrimination occur and implement it for a variety of e-commerce web sites. While it may seem conceptually simple to detect differences between users' results, accurately attributing these differences to price discrimination and steering requires correctly addressing a number of sources of noise. Second, we use the accounts and cookies of over 300 real-world users to detect price steering and discrimination on 16 popular e-commerce sites. We find evidence for some form of personalization on nine of these e-commerce sites. Third, we investigate the effect of user behaviors on personalization. We create fake accounts to simulate different user features including web browser/OS choice, owning an account, and history of purchased or viewed products. Overall, we find numerous instances of price steering and discrimination on a variety of top e-commerce sites.

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“…This has motivated researchers to begin measuring the personalization present in deployed systems, such as web search engines [17,27,50] and recommender systems [4].…”

Section: The Filter Bubblementioning

confidence: 99%

Measuring Price Discrimination and Steering on E-commerce Web Sites

Hannák

Soeller

Lazer

et al. 2014

Proceedings of the 2014 Conference on Internet Measurement Conference

227

144

View full text Add to dashboard Cite

show abstract

“…Bakalov et al (2013) proposed an approach to controlling adaptive behavior in recommender systems by allowing users to get an overview of personalization effects, view the user profile that is used for personalization, and adjust the profile and personalization effects to their needs and preferences. Bakalov et al (2013) proposed an approach to controlling adaptive behavior in recommender systems by allowing users to get an overview of personalization effects, view the user profile that is used for personalization, and adjust the profile and personalization effects to their needs and preferences.…”

Section: Usabilitymentioning

confidence: 99%

“…One criticism of adaptive/personalization systems is their potential for violation of the usability principles of direct manipulation systems, that is, controllability, predictability, transparency, and unobtrusiveness. Bakalov et al (2013) proposed an approach to controlling adaptive behavior in recommender systems by allowing users to get an overview of personalization effects, view the user profile that is used for personalization, and adjust the profile and personalization effects to their needs and preferences. A user study evaluating a biomedical literature system portal with seven participants showed that users favored the controllable personalization on the usefulness, usability, user satisfaction, transparency, and trustworthiness of personalized systems.…”

Section: Usabilitymentioning

confidence: 99%

Personalization in text information retrieval: A survey

Liu

Belkin

2019

Asso for Info Science & Tech

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Personalization of information retrieval (PIR) is aimed at tailoring a search toward individual users and user groups by taking account of additional information about users besides their queries. In the past two decades or so, PIR has received extensive attention in both academia and industry. This article surveys the literature of personalization in text retrieval, following a framework for aspects or factors that can be used for personalization. The framework consists of additional information about users that can be explicitly obtained by asking users for their preferences, or implicitly inferred from users' search behaviors. Users' characteristics and contextual factors such as tasks, time, location, etc., can be helpful for personalization. This article also addresses various issues including when to personalize, the evaluation of PIR, privacy, usability, etc. Based on the extensive review, challenges are discussed and directions for future effort are suggested.

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“…Work by Bull et al [10], Dimitrova [11], and Zapata-Rivera and Greer [12] on open learner modeling falls into this stream and more recently, work by Bakalov et al [13]. More recently, Bakalov et al [14], and Parra et al [15] have expanded this work into the area of recommender system by visualizing user models and allowing users to manipulate them and control the system recommendation process.…”

Section: Related Workmentioning

confidence: 99%

Understanding and controlling the filter bubble through interactive visualization

Nagulendra

Vassileva

2014

Proceedings of the 25th ACM Conference on Hypertext and Social Media

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The "filter bubble" is a term which refers to people getting encapsulated in streams of data such as news or social network updates that are personalized to their interests. While people need protection from information overload and maybe prefer to see content they feel familiar or agree with, there is the danger that important issues that should be of concern for everyone will get filtered away and people will lack exposure to different views, living in "echo-chambers", blissfully unaware of the reality. We have proposed a design of an interactive visualization, which provides the user of a social networking site with awareness of the personalization mechanism (the semantics and the source of the content that is filtered away), and with means to control the filtering mechanism. The visualization has been implemented in a peer-to-peer social network, called MADMICA, and we present here the results of a large scale lab study with 163 crowd-sourced participants. The results demonstrate that the visualization leads to increased users' awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over their data stream.

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An approach to controlling user models and personalization effects in recommender systems

Cited by 41 publications

References 15 publications

Measuring Price Discrimination and Steering on E-commerce Web Sites

Measuring Price Discrimination and Steering on E-commerce Web Sites

Personalization in text information retrieval: A survey

Understanding and controlling the filter bubble through interactive visualization

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