Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.

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Introduction to Recommender Systems Handbook

Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware recommender systems.

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Context-Aware Recommender Systems

The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). In this chapter we argue that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender systems. Furthermore, we introduce three different algorithmic paradigms – contextual prefiltering, post-filtering, and modeling – for incorporating contextual information into the recommendation process, discuss the possibilities of combining several contextaware recommendation techniques into a single unifying approach, and provide a case study of one such combined approach. Finally, we present additional capabilities for context-aware recommenders and discuss important and promising directions for future research.

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Context-aware recommender systems

Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed.

Context-Aware Recommender Systems for Learning: A Survey and Future Challenges

As wireless communication advances, research on location-based services using mobile devices has attracted interest, which provides information and services related to user's physical location. As increasing information and services, it becomes difficult to find a proper service that reflects the individual preference at proper time. Due to the small screen of mobile devices and insufficiency of resources, personalized services and convenient user interface might be useful. In this paper, we propose a map-based personalized recommendation system which reflects user's preference modeled by Bayesian Networks (BN). The structure of BN is built by an expert while the parameter is learned from the dataset. The proposed system collects context information, location, time, weather, and user request from the mobile device and infers the most preferred item to provide an appropriate service by displaying onto the mini map.

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Location-based recommendation system using Bayesian user's preference model in mobile devices

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.

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Context-Aware Recommendations in the Mobile Tourist Application COMPASS

Service discovery is a process of locating, or discovering, one or more documents, that describe a particular service. Most of the current service discovery approaches perform syntactic matching, that is, they retrieve services descriptions that contain particular keywords from the user’s query. This often leads to poor discovery results, because the keywords in the query can be semantically similar but syntactically different, or syntactically similar but semantically different from the terms in a service description. Another drawback of the existing service discovery mechanisms is that the query-service matching score is calculated taking into account only the keywords from the user’s query and the terms in the service descriptions. Thus, regardless of the context of the service user and the context of the services providers, the same list of results is returned in response to a particular query. This paper presents a novel approach for service discovery that uses ontologies to capture the semantics of the user’s query, of the services and of the contextual information that is considered relevant in the matching process.

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Context-aware, ontology-based, service discovery

Existing context-aware mobile applications often rely on location information. However, raw location data such as GPS coordinates or GSM cell identifiers are usually meaningless to the user and, as a consequence, researchers have proposed different methods for inferring so-called places from raw data. The places are locations that carry some meaning to user and to which the user can potentially attach some (meaningful) semantics. Examples of places include home, work and airport. A lack in existing work is that the labeling has been done in an ad hoc fashion and no motivation has been given for why places would be interesting to the user. As our first contribution we use social identity theory to motivate why some locations really are significant to the user. We also discuss what potential uses for location information social identity theory implies. Another flaw in the existing work is that most of the proposed methods are not suited to realistic mobile settings as they rely on the availability of GPS information. As our second contribution we consider a more realistic setting where the information consists of GSM cell transitions that are enriched with GPS information whenever a GPS device is available. We present four different algorithms for this problem and compare them using real data gathered throughout Europe. In addition, we analyze the suitability of our algorithms for mobile devices.

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Identifying meaningful locations

The web has evolved from the HTML-based repositories of text and images towards the current web services and semantic web developments. Web services are believed to help the integration of diverse applications while the semantic web promises to increase the "intelligence" of the web, enabling richer discovery, data integration, navigation and automation of tasks. A natural next step would be the combination of these developments, resulting in semantic web services. Another trend we observe is the development of next generation networks, such as GPRS and UMTS, which provide a richer environment as they will have access to information about the user�s, location, preferences and changing environment. With the aid of this information, service providers will be able to develop services that adapt to the user�s context. However, current web services technologies, based on WSDL and UDDI, do not provide a means for context-aware service discovery. In this paper we present the design and implementation of an enhanced UDDI server, capable of storage, matching and retrieval of semantically rich service profiles that contain contextual information. We also present a tool that facilitates the creation, publication and discovery of such service profiles.

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Extending UDDI with Context-Aware Features Based on Semantic Service Descriptions

We present the Context Watcher, a mobile application that enables mobile phone users to easily and unobtrusively share personal context data such as their location, heart rate, speed, or view, with their mutual consent. Not only can the data be shared, it can also be used as input for information services, to adapt applications to the context, or to automatically derive daily patterns and situational information, such as ‘meeting with a supervisor’, ‘is a regular visitor’ or ‘having the best condition of all your friends’. The application is built atop of the MobiLife context management framework, a generic approach enabling context discovery, exchange and reasoning. In the context management framework, different entities, context providers, are exposed to and interact via the internet. The context management framework and its principles will be discussed in detail, as well as the different context providers that play a role in the Context Watcher application. The Context Watcher has a large user base of 100+ users, some of which are 24x7 power-users taking the Context Watcher wherever they go, enabling the research on daily patterns and situations to work with real-life data, resulting in powerful algorithms and better understanding of context and context-awareness.

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Johan Koolwaaij

Papers

Context-Aware Recommendations in the Mobile Tourist Application COMPASS

Context-aware, ontology-based, service discovery

Identifying meaningful locations

Extending UDDI with Context-Aware Features Based on Semantic Service Descriptions

Context Watcher ─ Sharing context information in everyday life