Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context-Aware Recommender Systems <s> This paper describes the context-aware mobile tourist application COMPASS that adapts its services to the user’s needs based on both the user’s interests and his current context. In order to provide context-aware recommendations, a recommender system has been integrated with a context-aware application platform. We describe how this integration has been accomplished and how users feel about such an adaptive tourist application. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context-Aware Recommender Systems <s> A context-aware media recommendation platform uses an NtimesM-dimensional model and a hybrid processing approach to support media recommendation, adaptation, and delivery for smart phones. To provide media recommendations for smart phones based on all three context categories, we present a generic and flexible NtimesM-dimensional (N2M) recommendation model. The model considers context information ranging from user preference and situation to device and network capability as input for both content and presentation recommendations. We propose a hybrid recommendation approach to synergize content-based, Bayesian-classifier, and rule-based methods. Based on the recommendation model and hybrid-processing approach, we built a context-aware media recommendation platform called CoMeR to support media recommendation, adaptation, and delivery for smart phones <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context-Aware Recommender Systems <s> As the World Wide Web becomes a large source of digital music, the music recommendation system has got a great demand. There are several music recommendation systems for both commercial and academic areas, which deal with the user preference as fixed. However, since the music preferred by a user may change depending on the contexts, the conventional systems have inherent problems. This paper proposes a context-aware music recommendation system (CA-MRS) that exploits the fuzzy system, Bayesian networks and the utility theory in order to recommend appropriate music with respect to the context. We have analyzed the recommendation process and performed a subjective test to show the usefulness of the proposed system. <s> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context-Aware Recommender Systems <s> Recently, context-aware recommender systems (CARS), which incorporates contextual information into recommender systems, has become one of the hottest topics in the domain of recommender systems. In this paper, we identify and discuss some challenges for context-aware recommender systems, including viewing it as a process, valid contexts discovering and computing, contextual user preference elicitation, classification and design of context-aware recommendation algorithms, lack of publicly available datasets, evaluation, the sparsity problem, taking account of interdisciplinary research and applications. If these issues can be properly addressed, the development of context-aware recommender systems, in our opinion, will be promoted significantly. <s> BIB004 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context-Aware Recommender Systems <s> Recommender systems are commonly used for recommending items such as products, restaurants or other points-of-interest (POI). In our automotive scenario, the driver of a car gets recommendations for gas stations. Thereby, item attributes such as price or location are important, but also context data such as the current time, location or gas level of the car when requesting the recommendation. Our approach is based on Multi-Criteria Decision Making (MCDM) methods to calculate scores on several dimensions. We used utility functions modeling the importance of different route context elements which were derived from a preliminary user study, among other information. In addition, our system performs contextual pre- and post-filtering to reduce the number of considered items. The evaluation showed that our approach produced reasonably good results in comparison with the assessment of users in a second study. <s> BIB005
Traditionally, collaborative, content-based, knowledgebased, and hybrid recommender systems deal with two types of entities, users and items. As discussed in Section 2.1.1, TEL applications have inherent additional complexities and may not fit well into the traditional 2D user/item approach based on ratings only. Of interest for TEL recommenders is the incorporation of additional information about learners and teachers and their context in the recommendation process. Such data can be used to adapt recommendations based on individual learner characteristics, such as learning goals and knowledge levels, and additional contextual information such as available time, location, people nearby, etc. Pioneering work on context-aware recommender systems has been done by Adomavicius and Tuzhilin  . The authors researched approaches where the traditional user/ item paradigm was extended to support additional dimensions capturing the context in which recommendations are made. Such contextual information can be obtained in a number of ways. . Explicit context capturing relies on manual input from users. Registration modules are often used to capture information of users or rating modules are used to retrieve interests and preferences. . Implicit methods capture contextual information automatically from the environment, for instance by obtaining the current location or device type. . Contextual information can also be inferred by analyzing user interactions with tools and resources, for instance to estimate the current task of the user. Different paradigms have been proposed to incorporate contextual information in the recommendation process. A first recommendation via context-driven querying and search approach uses contextual information to query or search a certain repository of resources (e.g., restaurants) and presents the best matching resources (e.g., nearby restaurants that are currently open) to the user. A second contextual preference elicitation and estimation approach is a more recent trend in context-aware recommender system research. This approach attempts to model and learn contextual user preferences. These recommender systems are built on knowledge of partial contextual user preferences and typically deal with data records of the form <user, item, context, rating> . Each record therefore captures how much a user liked a particular item in a specific context (e.g., weekend or weekday). Three approaches have been identified to deal with such contextual preferences. In a contextual prefiltering approach, contextual information is used to filter the data set before applying a traditional recommendation algorithm. In a contextual postfiltering approach, recommendations are generated on the entire data set. The resulting set of recommendations is adjusted using the contextual information. Contextual modeling approaches use contextual information directly in the recommendation function as an explicit predictor of a rating for an item. Whereas contextual prefiltering and postfiltering approaches can use traditional recommendation algorithms, the contextual modeling approach uses multidimensional recommendation algorithms. Examples of heuristic-based and model-based approaches have been described in  . Several contextual recommender systems have been developed that use these paradigms in various application domains. Examples include context-aware recommender systems that suggest gas stations to a driver of a car BIB005 , contextualized media delivery systems BIB002 , BIB003 and intelligent tourist guides  . For example, COMPASS BIB001 is a recommender system that uses a context-driven querying and search approach to provide a tourist with information about nearby monuments, hotels and people. In an evaluation experiment, time and location were used to contextualize recommendations. Interestingly, the authors report that "last time visited" had a negative influence on the perceived usefulness of the system. These results illustrate that careful analysis of data that is taken into account is necessary when deploying contextualization algorithms. The influence of various parameters on the recommendation process is therefore currently of major interest. This challenge has been identified by several authors. Yujie and Licai BIB004 outline that it is difficult to describe clearly and uniformly what types of contexts are truly needed in CARS because of the variety of application scenarios and user needs. Discovering valid context types and instances and then implementing them are therefore serious challenges that CARS should face and resolve. Adomavicius and Tuzhilin  identify the development of high-performing context-aware recommender systems and testing them on practical applications as an important challenge. They argue that most work on context-aware recommender systems has been conceptual, where a certain method has been developed, tested on some (often limited) data, and shown to perform well in comparison to certain benchmarks. Among others, they argue that there has been little work done on developing novel data structures and new system architectures for CARS that incorporate context sensors and various filters and converters in a modular fashion. A third important challenge is the evaluation and lack of publicly available data sets  , BIB004 . In order to assess the impact of various contextual parameters, data sets are needed that contain contextual data. The interest in this area is reflected by the organization of several workshops and challenges related to, among others, contextual movie recommendation that have been organized in recent years  . Overall, the field of context-aware recommender systems is promising, but much work is needed to explore it comprehensively. In this paper, we explore the challenges outlined above for the development of CARS for learning. As a first step in this direction, we define a context framework for TEL that identifies relevant context categories for the analysis and development of CARS for learning. Then, we present an analysis of CARS that have been deployed in recent years within the TEL domain. In particular, we focus on context dimensions that are used to contextualize recommendations, context sensors that capture contextual information, contextual recommendation algorithms and evaluation methods and results.