Addressing Users’ Privacy Concerns for Improving Personalization Quality: Towards an Integration of User Studies and Algorithm Evaluation

Berendt, Bettina; Teltzrow, Maximilian

doi:10.1007/11577935_4

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…users' ratings) to give good recommendations. Privacy concerns may reduce users' tendency to disclose personal information (Teltzrow and Kobsa 2004;Chellappa and Sin 2005;Berendt and Teltzrow 2005;Ackerman et al 1999). On the other hand, if it positively influences their user experience (i.e.…”

Section: Personal and Situational Characteristics In Contextmentioning

confidence: 99%

Explaining the user experience of recommender systems

Knijnenburg

Willemsen

Gantner

et al. 2012

User Model User-Adap Inter

580

340

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Research on recommender systems typically focuses on the accuracy of prediction algorithms. Because accuracy only partially constitutes the user experience of a recommender system, this paper proposes a framework that takes a user-centric approach to recommender system evaluation. The framework links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively). Furthermore, it incorporates the influence of personal and situational characteristics on the user experience. This paper reviews how current literature maps to the framework and identifies several gaps in existing work. Consequently, the framework is validated with four field trials and two controlled experiments and analyzed using Structural Equation Modeling. The results of these studies show that subjective system aspects and experience variables are invaluable in explaining why and how the user experience of recommender systems comes about. In all studies we observe that perceptions of recommendation quality and/or variety are important mediators in predicting the effects of objective system aspects on the three components of user experience: process (e.g. perceived effort, difficulty), system (e.g. perceived system effectiveness) and outcome (e.g. choice satisfaction). Furthermore, we find that these subjective aspects have strong and sometimes interesting behavioral correlates (e.g. reduced browsing indicates higher system effectiveness). They also show several tradeoffs between system aspects and personal and situational characteristics (e.g. the amount of preference feedback users provide is a tradeoff between perceived system usefulness and privacy concerns). These results, as well as the validated framework itself, provide a platform for future research on the user-centric evaluation of recommender systems.

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Section: Personal and Situational Characteristics In Contextmentioning

confidence: 99%

Explaining the user experience of recommender systems

Knijnenburg

Willemsen

Gantner

et al. 2012

User Model User-Adap Inter

580

340

View full text Add to dashboard Cite

show abstract

“…(Wang & Kobsa, 2007) Personalized systems need to care about the privacy concerns of the user. According to Bettina and Teltzrow (2005) the existing systems are based on relatively complex syntactic methods, which skip a lot of information during the description of the references. All the existing personalization techniques select recommendations based on syntactic instruments, which lead to missing out on big amounts of information regarding the user's preferences.…”

Section: Problemsmentioning

confidence: 99%

“…To address the end user's privacy concerns, the study of Berendt and Teltzrow (2005) aimed at improving the web personalization proposals (the quality and performance), and gave more attention to the privacy problems of the user. Recently, many approaches have been available.…”

Section: Applying Human Computer Interaction To Privacymentioning

confidence: 99%

Implementing End-User Privacy through Human Computer Interaction for Improving Quality of Personalized Web

Abu-Dalbouh

2016

CIS

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Users are exposed to an overwhelming amount of information in several application domains. There is a remarkable increase in the usage of Internet and general technology improvements. From this, it follows that there is a demand and need to create personalized web systems. Thus, personalized web systems are a good method of handling the flood of information and information overload, by helping people to surf the net and look for what they need. Unfortunately, the privacy issues that end-users face have not been taken seriously by some of these personalized web systems, as some of them apply it partially or fail to address the privacy issues in personalization. This affects the quality of the existing personalized web. The paper aims to investigate and explain the reasons behind the end-user's fears in giving out his/her personal information on personalized websites. Then, based on the results modifying approach of personalized web through the use of Human Computer Interaction models to enhance the quality of the current personalized web. By addressing the main privacy issues in human computer interaction that helps the end-user trust the personalization web and website with his/her personal information and improve the quality of current personalized web. A survey study with 134 Internet users was conducted. The findings show that personalization plays different roles of attracting users in personalized websites. Internet users want useful personalized services, but at the same time, they are concerned with the manner in which firms use their data for personalization. If the involvement of Internet users with their current site is high, then personalized services are not attractive enough to motivate them to give their personal information.

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“…These profiles may be based on demographics, user purchasing behavior, or user navigational behavior. Users imparting this information to personalization systems are becoming increasingly concerned about the security of such data and invasion of their privacy [Berendt and Teltzrow 2005]. Hence an important research direction in Web personalization is the use of privacy-preserving personalization [Canny 2002].…”

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confidence: 99%

Introduction to intelligent techniques for Web personalization

Anand

Mobasher

2007

ACM Trans. Internet Technol.

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Web personalization [Anand and Mobasher 2005] can be defined as any set of actions that can tailor the Web experience to a particular user or set of users. The experience can be something as casual as browsing a Web site or as (economically) significant as trading stocks or purchasing a car. The actions can range from simply making the presentation more pleasing to anticipating the needs of a user and providing customized and relevant information. To achieve effective personalization, organizations must rely on all available data, including the usage and click-stream data (reflecting user behavior), site content, site structure, and domain knowledge, as well as user demographics and profiles. Efficient and intelligent techniques are needed to mine this data for actionable knowledge, and to effectively use the discovered knowledge to enhance the users' Web experience.Being data driven; Web personalization is also achieved through the implementation of all the phases of a typical data mining cycle [Mobasher 2007] including data collection, preprocessing, pattern discovery and evaluation, in an off-line mode, and finally the deployment of the knowledge in realtime to mediate between the user and the Web. The data collection and preprocessing phases involve intelligently extracting and integrating useful data from multiple sources and extracting user preferences implicit within the data. The pattern discovery phase usually involves the adaptation and integration of techniques from machine learning, information retrieval and filtering, databases, agent architectures, knowledge representation, data mining, text mining, statistics, information security and privacy, and context modeling with the goal of building single or group user models. These techniques must address Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications important challenges emanating from the size of the data and the fact that they are heterogeneous and very personal in nature, as well as the dynamic nature of user interactions with the Web. Evaluation of the user models learned from the data involves the estimation of the accuracy of the models for predicting content that may be interesting to a user as well as other aspects such as explainability of the recommendations, diversity of the recommendation set, serendipity of the recommendations, and user satisfaction [Herlocker et al. 2004].Personalization systems depend on access to user profiles...

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Addressing Users’ Privacy Concerns for Improving Personalization Quality: Towards an Integration of User Studies and Algorithm Evaluation

Cited by 9 publications

References 22 publications

Explaining the user experience of recommender systems

Explaining the user experience of recommender systems

Implementing End-User Privacy through Human Computer Interaction for Improving Quality of Personalized Web

Introduction to intelligent techniques for Web personalization

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