Mobile Multimedia Recommendation in Smart Communities: A Survey <s> 2) Ubiquity <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> BIB001 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> 2) Ubiquity <s> With the increasing amount of digital multimedia content, the user is more and more overstrained. A promising solution for this problem is personalization that assists the user in selecting content with respect to the user's interest. Since the capabilities of mobile devices increased significantly in the recent years, the number of mobile multimedia services grows steadily. The mobile services are confronted with a much more changing environment than stationary services. This additional information enables even more precise personalization. This paper introduces an approach for context-aware personalization of mobile multimedia services. We developed a generic framework for application developers that can easily be configured and extended with application-specific algorithms matching content and context. In the proposed system, content selection is performed in a distributed way. <s> BIB002 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> 2) Ubiquity <s> Existing recommender systems provide an elegant solution to the information overload in current digital libraries such as the Internet archive. Nowadays, the sensors that capture the user's contextual information such as the location and time are become available and have raised a need to personalize recommendations for each user according to his/her changing needs in different contexts. In addition, visual documents have richer textual and visual information that was not exploited by existing recommender systems. In this paper, we propose a new framework for context-aware recommendation of visual documents by modeling the user needs, the context and also the visual document collection together in a unified model. We address also the user's need for diversified recommendations. Our pilot study showed the merits of our approach in content based image retrieval. <s> BIB003 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> 2) Ubiquity <s> The multimedia content created by users with their mobile phones is often shared with family and friends to recreate personal experiences. It is difficult for a single media sharing service to cover all variations on how people would like to present their experiences of different events; thus it is important to be able to easily create versions of these services. This paper presents in detail our platform implementation for enabling the creation of lightweight content- and context-aware mobile multimedia services. The platform supports state-of-the-art content management functionalities in order to enable easy creation of specialized multimedia services for various target groups and purposes. Our solution includes context metadata support for mobile multimedia content and creation of location-aware multimedia services. We have built example services on top of the platform using web-based technologies such as JavaScript, Media RSS feed and Java. The functionality of the platform has been tested in user evaluations with promising results. <s> BIB004 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> 2) Ubiquity <s> Museums offer services to enhance its visitors' tour experiences. Examples of these kinds of assistance are tour guides, film showings, and even the use of different technologies such as kiosks and audio systems. However, although these facilities provide interactive service, none of them offers personalized service. In this research, we studied the comparison of four major recommender system approaches: content-based, content-based with context, collaborative, and collaborative with context to provide personalized recommendations to museum visitors. Based on the result of the comparisons, we implemented a recommender system for a mobile museum guide called MyMuseum. In this paper, we discuss MyMuseum's functionalities and compare them with other systems. We also present results of our testing and conclude that MyMuseum is a useful application that museum visitors can use during their tour. <s> BIB005 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> 2) Ubiquity <s> Mobile TV provides multimedia contents to the user by wireless communication. Here the personalized recommendation system allows the users to easily select the contents wanting to access. In this paper we propose a personalized recommendation scheme which considers the activities of the user at runtime and the information on the environment around the user. It allows efficient operation in mobile device, and interoperability between the TV multimedia metadata and ontology. The accuracy of the proposed scheme is evaluated by an experiment, which reveals a significant improvement compared to the existing schemes. <s> BIB006 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> 2) Ubiquity <s> In this paper, we describe the project SNOPS, a smart-city environment based on Future Internet technologies. We focused on the context-aware recommendation services provided in the platform, which accommodates location dependent multimedia information with user's needs in a mobile environment related to an outdoor scenario within the Cultural Heritage domain. In particular, we describe a recommendation strategy for planning browsing activities exploiting objects features, users' behaviors and context information gathered by apposite sensor networks. Preliminary experimental results, related to user's satisfaction, have been carried out and discussed. <s> BIB007
The second exclusive property is "ubiquity". Ubiquity is the ability of mobile multimedia recommender systems to deliver the multimedia information and services to mobile device users wherever they are and whenever they need. Original recommendation classifications and computation models that are applicable in multimedia have been explicitly developed for mobile recommender systems. In the field of mobile multimedia recommender systems, various types of context such as computing devices and networks, location, physical conditions, temperature, weather, and time are very important and need to be considered and incorporated in a recommendation process. Here we elaborate on some related works and existing research conducted in the area of mobile multimedia recommender systems that focus on context-aware scenarios. Yong et al. BIB006 proposed a personalized recommendation scheme which considers the activities of the user at runtime and the information on the environment around the user. It allows efficient operation in mobile devices, and interoperability between the TV multimedia metadata and ontology. The accuracy of the proposed scheme was evaluated by an experiment, which revealed a significant improvement compared to the existing schemes. The study in BIB007 describes the project SNOPS, a smart-city environment based on Future Internet technologies. The authors therein focused on the context-aware recommendation services provided in the platform, which accommodates location dependent multimedia information with user's needs in a mobile environment related to an outdoor scenario within the cultural heritage domain. In particular, they described a recommendation strategy for planning browsing activities, exploiting object features, users' behaviors and context information gathered by opposite sensor networks. Preliminary experimental results, related to user's satisfaction, have been carried out and discussed. In BIB005 the comparison of four major recommender system approaches: content-based, content-based with context, collaborative with context and collaborative were studied and used to provide personalized recommendations to museum visitors. Based on the result of the comparisons, the authors implemented a recommender system for a mobile museum guide called MyMuseum. Discussions were presented on My Museum's functionalities and comparison was made with MyMuseum and other systems. The authors also presented results of their testing and concluded that MyMuseum is a useful application that museum visitors can use during their tours. The study in BIB002 introduced an approach for context-aware personalization of mobile multimedia services. The authors developed a generic framework for application developers that can easily be configured and extended with application-specific algorithms matching content and context. In the proposed system, content selection is performed in a distributed way. The study in BIB004 presented in detail a platform implementation for enabling the creation of lightweight content-and context-aware mobile multimedia services. The platform supports content management functionalities in order to enable easy creation of specialized multimedia services for various target groups and purposes. The solution includes context metadata support for mobile multimedia content and creation of location-aware multimedia services. In BIB003 a new framework for context-aware recommendation of visual documents by modeling the user needs, the context, and the visual document collection together in a unified model was proposed. The user's needs for diversified recommendations were addressed. The pilot study showed the merits of the approach in using content based image retrieval. To provide media recommendations for smart phones based on all three context categories, the authors of BIB001 presented 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.