Challenges in context-aware mobile language learning: the MASELTOV approach

Smartphones, as highly portable networked computing devices with embedded sensors including GPS receivers, are ideal platforms to support context-aware language learning. They can enable learning when the user is en-gaged in everyday activities while out and about, complementing formal language classes. A significant challenge, however, has been the practical implementation of services that can accurately identify and make use of context, particularly location, to offer meaningful language learning recommendations to users. In this paper we review a range of approaches to identifying context to support mobile language learning. We consider how dynamically changing aspects of context may influence the quality of recommendations presented to a user. We introduce the MASELTOV project’s use of context awareness combined with a rules-based recommendation engine to present suitable learning content to recent immigrants in urban areas; a group that may benefit from contextual support and can use the city as a learning environment

Hybrid group recommendations for a travel service

Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations

Adversarial Training Towards Robust Multimedia Recommender System

With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advance on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in https://github.com/duxy-me/AMR.Comment: TKD

Bletchley Park text: using mobile and semantic web technologies to support the post-visit use of online museum resources

A number of technologies have been developed to support the museum visitor, with the aim of making their visit more educationally rewarding and/or entertaining. Examples include PDA-based personalized tour guides and virtual reality representations of cultural objects or scenes. Rather than supporting the actual visit, we decided to employ technology to support the post-visitor, that is, encourage follow-up activities among recent visitors to a museum. This allowed us to use the technology in a way that would not detract from the existing curated experience and allow the museum to provide access to additional heritage resources that cannot be presented during the physical visit. Within our application, called Bletchley Park Text, visitors express their interests by sending text (SMS) messages containing suggested keywords using their own mobile phone. The semantic description of the archive of resources is then used to retrieve and organize a collection of content into a personalized web site for use when they get home. Organization of the collection occurs both bottom-up from the semantic description of each item in the collection, and also top-down according to a formal representation of the overall museum story. In designing the interface we aimed to support exploration across the content archive rather than just the search and retrieval of specific resources. The service was developed for the Bletchley Park museum and has since been launched for use by all visitors

Travel Package Recommendation

Location Based SocialNetworks (LBSN) benefit the users by allowing them to share their locations and life moments with their friends. The users can also review the locations they have visited. Classical recommender systems provide users a ranked list of single items. This is not suitable for applications like trip planning,where the recommendations should contain multiple items in an appropriate sequence. The problem of generating such recommendations is challenging due to various critical aspects, which includes user interest, budget constraints and high sparsity in the available data used to solve the problem. In this paper, we propose a graph based approach to recommend a set of personalized travel packages. Each recommended package comprises of a sequence of multiple Point of Interests (POIs). Given the current location and spatio-temporal constraints, our goal is to recommend a package which satisfies the constraints. This approach utilizes the data collected fromLBSNs to learn user preferences and also models the location popularity