Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Interaction Challenges <s> Automated collaborative filtering (ACF) systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people and sharing ratings between like-minded persons. However, current recommender systems are black boxes, providing no transparency into the working of the recommendation. Explanations provide that transparency, exposing the reasoning and data behind a recommendation. In this paper, we address explanation interfaces for ACF systems - how they should be implemented and why they should be implemented. To explore how, we present a model for explanations based on the user's conceptual model of the recommendation process. We then present experimental results demonstrating what components of an explanation are the most compelling. To address why, we present experimental evidence that shows that providing explanations can improve the acceptance of ACF systems. We also describe some initial explorations into measuring how explanations can improve the filtering performance of users. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Interaction Challenges <s> The MOBIlearn project aims to develop a re-usable architecture for delivering mobile learning experiences. A key component of this architecture is a context-awareness subsystem that is intended to tailor the content and options made available to a learner, depending on their current situation, preferences, and learning history. The context awareness subsystem has been developed alongside a hierarchical model of context, and has been subjected to formative evaluation. With reference to our context model, preliminary user trials, and input from museum staff, we describe the planned deployment of this system in an art museum learning scenario. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Interaction Challenges <s> This paper describes a computer supported ubiquitous learning environment for language learning. This paper proposes two systems. The first is context-aware language-learning support system for Japanese polite expressions learning, which is called JAPELAS (Japanese polite expressions learning assisting system). This system provides learner the appropriate polite expressions deriving the learner's situation and personal information. The second system is called TANGO (Tag Added learNinG Objects) system, which detects the objects around learner using RFID tags, and provides the learner the educational information. This paper describes the preliminary evaluation of those two systems. <s> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Interaction Challenges <s> This paper introduces a ubiquitous computing environment in order to support the learners while doing tasks; this environment is called PERKAM (PERsonalized Knowledge Awareness Map). PERKAM allows the learners to share knowledge, interact, collaborate, and exchange individual experiences. It utilizes the RFID ubiquities technology to detect the learner’s environmental objects and location, then recommends the best matched educational materials and peer helpers in accordance with the detected objects and the current location. This environment provides the learner with Knowledge Awareness Map, which visualizes the space of the environmental objects that surround the learner, the educational materials space, and the peer helpers’ space. PERKAM system was implemented and an experiment was done in order to evaluate the system performance and the learner’s satisfaction. This paper illustrates the design, the implementation and the evaluation of this environment, and it focuses on Knowledge Awareness Map, which is personalized according to the learner’s current need and location. <s> BIB004 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Interaction Challenges <s> Collaborative filtering (CF) has been successfully deployed over the years to compute predictions on items based on a user's correlation with a set of peers. The black-box nature of most CF applications leave the user wondering how the system arrived at its recommendation. This note introduces PeerChooser, a collaborative recommender system with an interactive graphical explanation interface. Users are provided with a visual explanation of the CF process and opportunity to manipulate their neighborhood at varying levels of granularity to reflect aspects of their current requirements. In this manner we overcome the problem of redundant profile information in CF systems, in addition to providing an explanation interface. Our layout algorithm produces an exact, noiseless graph representation of the underlying correlations between users. PeerChooser's prediction component uses this graph directly to yield the same results as the benchmark. User's then improve on these predictions by tweaking the graph to their current requirements. We present a user-survey in which PeerChooser compares favorably against a benchmark CF algorithm. <s> BIB005 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Interaction Challenges <s> Social bookmarking tools are very popular nowadays. In most tools, users tag the bookmarks to describe them. Therefore, it is of- ten hard for users to discover implicit structures between tags, users and bookmarks. We think that this is essential for both end users to discover new bookmarks that could be of interest to them, and for researchers who want to study how people use social information re- trieval tools. In this work, a cluster map visualisation technique is customized to enable users to explore social bookmarks in the del.icio.us and the CALIBRATE system. The design of our visualisation aims to automatically identify tag and community structures, and visualises these structures in order to increase the users awareness of them. <s> BIB006 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Interaction Challenges <s> Content-centric social websites, such as discussion forums and blog sites, have flourished during the past several years. These sites often contain overwhelming amounts of information that are also being updated rapidly. To help users locate their interests at such sites (e.g., interesting blogs to read or discussion forums to join), researchers have developed a number of recommendation technologies. However, it is difficult to make effective recommendations for new users (a.k.a. the cold start problem) due to a lack of user information (e.g., preferences and interests). Furthermore, the complexity of recommendation algorithms often prevents users from comprehending let alone trusting the recommended results. To tackle the above two challenges, we are building a social map-based recommender system called Pharos. A social map summarizes users' content-related social behavior over time (e.g., reading, writing, and commenting behavior during the past week) as a set of latent communities. Each community is characterized by the theme of the content being discussed and the key people involved. By discovering, ranking, and displaying the most "popular" latent communities, Pharos creates a visual social map of a website. This enables new users to obtain a quick overview of the site, alleviating the cold start problem. Furthermore, we use the social map as a context to help explain Pharos-recommended content and people. Users can also interactively explore the social map to locate their interested content or people that are not being explicitly recommended, compensating for the imperfection in the recommendation algorithms. We have deployed Pharos within our company and our preliminary evaluation shows the usefulness of Pharos. <s> BIB007
Several challenges related to user interfaces were identified by researchers in this field. Usability evaluations of the LIP  , MOBIlearn BIB002 , and TANGO BIB003 systems indicate that the development of user interfaces is critical for context-aware recommender systems. Among others, people are often confused because they do not understand why certain recommendations are made and why these recommendations change. As outlined in BIB001 , it is important to explain the rationale behind recommendations to end-users. The complexity of recommendation algorithms often prevents users from comprehending recommended results and can lead to trust issues when recommendations fail. This complexity is often aggravated by contextual recommendation algorithms that use various types of contextual information in the recommendation process. In addition, recommendation results might change automatically when the context of the user changes. Such automatic updates can be confusing to the user. To deal with this issue, it is important to provide explanations and justify decisions BIB005 . An important line of research in this area is the use of visualization techniques to provide users with insights in the recommendation process. As an example, social visualizations can help to explain recommendation results by explicitly exposing relationships among content and people BIB007 , BIB006 . The PERKAM BIB004 system already uses visualization techniques to present the relationship and distance between peer learners. Such visualizations can be used to easily locate suggested peer learners. Moreover, visualization techniques can increase understanding of the input and output of a recommender system. Such visualizations can enable the user to meaningfully revise input parameters and thus improve recommendations  . This objective is particularly important in contextualized recommendations that estimate relevant contextual elements based on user behavior. As the prediction of the current task or interest of the user is a challenging task, there is a need to develop mixed approaches that enable users to provide feedback and help steer this process. As such, the combination of visualization and recommendation techniques to empower users with actionable knowledge to become an active and responsible part-taker in the recommending process, instead of being the typical passive provider of just personal preferences and social connections, is a highly relevant research topic.