Evaluating recommender systems from the user’s perspective: survey of the state of the art <s> Privacy issues <s> Personalization has been a hot topic or nearly a decade now, and many new products and advanced algorithms have emerged in that time. Several companies now sell tools such as recommender systems, which take input about users and products and generate recommendations about which products the users will like best. At their best, recommenders can be wonderful tools for users, helping them sort through myriad items they could read, buy, or watch to select those few that are most valuable to them. The algorithms that power these systems have evolved dramatically, and the best can produce rapid recommendations over data sets of millions of users and hundreds of thousands of products. The other edge of the sword is that recommender systems provide perfect tools for marketers and others to invade users' privacy. After all, recommenders; seek to learn everything about our preferences, including what we like to read, what we like to buy, how much money we spend, and what influences us to spend it. How a recommender deals with privacy decides whether its-users view it,as a boon or a bane. If the recommender only uses this information to help us find items to purchase on a Web site, we will probably value the feature - it might even bring us back to shop there again. On the other hand, if the Web site sells our information to other companies, so they can more effectively bother us. with phone calls at dinner time, we'll probably feel our privacy has been invaded. Privacy is a critical issue for recommender systems. In the end, personalization is an important factor in developing effective Web sites because it creates a user experience that is both compelling and sticky. The experience is compelling because it helps users find exactly the information, products, and services they need. It is sticky because a personalized Web site trains itself over time to serve its users better, which makes those users less likely to go to a new site that they would have to train all over again. <s> BIB001 </s> Evaluating recommender systems from the user’s perspective: survey of the state of the art <s> Privacy issues <s> As electronic commerce environments become more and more interactive, privacy is a matter of increasing concern. Many surveys have investigated households' privacy attitudes and concerns, revealing a general desire among Internet users to protect their privacy. To complement these questionnaire-based studies, we conducted an experiment in which we compared self-reported privacy preferences of 171 participants with their actual disclosing behavior during an online shopping episode. Our results suggest that current approaches to protect online users' privacy, such as EU data protection regulation or P3P, may face difficulties to do so effectively. This is due to their underlying assumption that people are not only privacy conscious, but will also act accordingly. In our study, most individuals stated that privacy was important to them, with concern centering on the disclosure of different aspects of personal information. However, regardless of their specific privacy concerns, most participants did not live up to their self-reported privacy preferences. As participants were drawn into the sales dialogue with an anthropomorphic 3-D shopping bot, they answered a majority of questions, even if these were highly personal. Moreover, different privacy statements had no effect on the amount of information disclosed; in fact, the mentioning of EU regulation seemed to cause a feeling of 'false security'. The results suggest that people appreciate highly communicative EC environments and forget privacy concerns once they are 'inside the Web'. <s> BIB002 </s> Evaluating recommender systems from the user’s perspective: survey of the state of the art <s> Privacy issues <s> User-adaptive applications cater to the needs of each individual computer user, taking for example users' interests, level of expertise, preferences, perceptual and motoric abilities, and the usage environment into account. Central user modeling servers collect and process the information about users that different user-adaptive systems require to personalize their user interaction.Adaptive systems are generally better able to cater to users the more data their user modeling systems collect and process about them. They therefore gather as much data as possible and "lay them in stock" for possible future usage. Moreover, data collection usually takes place without users' initiative and sometimes even without their awareness, in order not to cause distraction. Both is in conflict with users' privacy concerns that became manifest in numerous recent consumer polls, and with data protection laws and guidelines that call for parsimony, purpose-orientation, and user notification or user consent when personal data are collected and processed.This article discusses security requirements to guarantee privacy in user-adaptive systems and explores ways to keep users anonymous while fully preserving personalized interaction with them. User anonymization in personalized systems goes beyond current models in that not only users must remain anonymous, but also the user modeling system that maintains their personal data. Moreover, users' trust in anonymity can be expected to lead to more extensive and frank interaction, hence to more and better data about the user, and thus to better personalization. A reference model for pseudonymous and secure user modeling is presented that meets many of the proposed requirements. <s> BIB003 </s> Evaluating recommender systems from the user’s perspective: survey of the state of the art <s> Privacy issues <s> Recommender systems are widely used to help deal with the problem of information overload. However, recommenders raise serious privacy and security issues. The personal information collected by recommenders raises the risk of unwanted exposure of that information. Also, malicious users can bias or sabotage the recommendations that are provided to other users. This paper raises important research questions in three topics relating to exposure and bias in recommender systems: the value and risks of the preference information shared with a recommender, the effectiveness of shilling attacks designed to bias a recommender, and the issues involved in distributed or peer-to-peer recommenders. The goal of the paper is to bring these questions to the attention of the information and communication security community, to invite their expertise in addressing them. <s> BIB004 </s> Evaluating recommender systems from the user’s perspective: survey of the state of the art <s> Privacy issues <s> This paper systematically evaluates the user experience of a recommender system. Using both behavioral data and subjective measures of user experience, we demonstrate that choice satisfaction and system effectiveness increase when a system provides personalized recommendations (compared to the same system that provides random recommendations). We furthermore demonstrate that despite privacy issues, this higher choice satisfaction and system effectiveness increases users’ intention to provide feedback about their preference. Due to an intention-behavior gap, this may however not necessarily influence the users’ actual feedback behavior. <s> BIB005
Privacy is a critical issue for recommender systems, regardless of whether the adopted user modeling method is explicit or implicit  BIB001 . To understand users well enough and make effective recommendations, a recommender must acquire sufficient information (e.g., demographic information, preference information, personality information etc.) about the users. The privacy concern becomes more important when the required information is more personal and users want to keep it confidential. Finding the optimal balance between privacy protection and personalization remains a challenging task. There is a general assumption that people are sensitive to privacy issues. However, a mismatch exists between people's privacy preferences and their actual behavior. BIB002 compared self-reported privacy preferences of 171 participants with their actual disclosing behavior during an online shopping episode. In their study, most individuals stated that privacy was important to them, with concern centering on the disclosure of different aspects of personal information. However, regardless of their specific privacy concerns, most participants did not adhere to their self-reported privacy preferences. The results suggest that people overlook privacy concerns once they are highly involved in the system. BIB005 validated the results of Spiekermann et al. They carried out a user study with 43 participants in the Microsoft ClipClub system (a multimedia recommender) with the aim of determining the factors that influence users' intention to provide feedback. Their results show that users' intention to provide feedback is influenced by both user experience (choice satisfaction, the perceived effectiveness of the system) and users' privacy concerns. That is, initial privacy concerns can be overcome when users perceive an improvement of their experience when they provide feedback. Moreover, studies show that explicitly indicating the resulting benefits could stimulate users to give more information  . Another factor which affects users' intentions to divulge personal information is their trust in the system BIB004 . Users must trust that the system will protect their information appropriately; reputed websites are able retain their users with considerable loyalty based on a high level of trust. For example, at Amazon, users are more apt to give personal information since they believe their information will be kept confidential. In addition, when users decide whether to provide personal information, they would like to know who is able to access the information and its specific purpose BIB003 . A serial of user studies conducted by Chen (2006, 2007) have indicated that explanation interfaces could effectively help build users' trust in the system. Other researchers also contend that explanation interfaces can cultivate user trust . Therefore, even though users are concerned with revealing their personal information, their initial privacy concerns can be overcome by the benefits. Thus, we propose: Guideline 10 Consider the tradeoff between users' privacy concerns and their willingness to reveal reference information. Designing explanation interfaces can be an effective method for achieving an optimal balance. The interfaces should explicitly indicate the benefits of providing information, as well as fully disclosing its use, in order to build users' trust in the system.