Advanced recommendations in a mobile tourist information system

An advanced tourist information provider system delivers information regarding sights and events on their users' travel route. In order to give sophisticated personalized information about tourist attractions to their users, the system is required to consider base data which are user preferences defined in their user profiles, user context, sights context, user travel history as well as their feedback given to the sighs they have visited. In addition to sights information, recommendation on sights to the user could also be provided. This project concentrates on combinations of knowledge on recommendation systems and base information given by the users to build a recommendation component in the Tourist Information Provider or TIP system. To accomplish our goal, we not only examine several tourist information systems but also conduct the investigation on recommendation systems. We propose a number of approaches for advanced recommendation models in a tourist information system and select a subset of these for implementation to prove the concept

i-JEN: Visual interactive Malaysia crime news retrieval system

Supporting crime news investigation involves a mechanism to help monitor the current and past status of criminal events. We believe this could be well facilitated by focusing on the user interfaces and the event crime model aspects. In this paper we discuss on a development of Visual Interactive Malaysia Crime News Retrieval System (i-JEN) and describe the approach, user studies and planned, the system architecture and future plan. Our main objectives are to construct crime-based event; investigate the use of crime-based event in improving the classification and clustering; develop an interactive crime news retrieval system; visualize crime news in an effective and interactive way; integrate them into a usable and robust system and evaluate the usability and system performance. The system will serve as a news monitoring system which aims to automatically organize, retrieve and present the crime news in such a way as to support an effective monitoring, searching, and browsing for the target users groups of general public, news analysts and policemen or crime investigators. The study will contribute to the better understanding of the crime data consumption in the Malaysian context as well as the developed system with the visualisation features to address crime data and the eventual goal of combating the crimes

Some like it hot - visual guidance for preference prediction

For people first impressions of someone are of determining importance. They are hard to alter through further information. This begs the question if a computer can reach the same judgement. Earlier research has already pointed out that age, gender, and average attractiveness can be estimated with reasonable precision. We improve the state-of-the-art, but also predict - based on someone's known preferences - how much that particular person is attracted to a novel face. Our computational pipeline comprises a face detector, convolutional neural networks for the extraction of deep features, standard support vector regression for gender, age and facial beauty, and - as the main novelties - visual regularized collaborative filtering to infer inter-person preferences as well as a novel regression technique for handling visual queries without rating history. We validate the method using a very large dataset from a dating site as well as images from celebrities. Our experiments yield convincing results, i.e. we predict 76% of the ratings correctly solely based on an image, and reveal some sociologically relevant conclusions. We also validate our collaborative filtering solution on the standard MovieLens rating dataset, augmented with movie posters, to predict an individual's movie rating. We demonstrate our algorithms on howhot.io which went viral around the Internet with more than 50 million pictures evaluated in the first month.Comment: accepted for publication at CVPR 201

Content Recommendation Through Linked Data

Nowadays, people can easily obtain a huge amount of information from the Web, but often they have no criteria to discern it. This issue is known as information overload. Recommender systems are software tools to suggest interesting items to users and can help them to deal with a vast amount of information. Linked Data is a set of best practices to publish data on the Web, and it is the basis of the Web of Data, an interconnected global dataspace. This thesis discusses how to discover information useful for the user from the vast amount of structured data, and notably Linked Data available on the Web. The work addresses this issue by considering three research questions: how to exploit existing relationships between resources published on the Web to provide recommendations to users; how to represent the user and his context to generate better recommendations for the current situation; and how to effectively visualize the recommended resources and their relationships. To address the first question, the thesis proposes a new algorithm based on Linked Data which exploits existing relationships between resources to recommend related resources. The algorithm was integrated into a framework to deploy and evaluate Linked Data based recommendation algorithms. In fact, a related problem is how to compare them and how to evaluate their performance when applied to a given dataset. The user evaluation showed that our algorithm improves the rate of new recommendations, while maintaining a satisfying prediction accuracy. To represent the user and their context, this thesis presents the Recommender System Context ontology, which is exploited in a new context-aware approach that can be used with existing recommendation algorithms. The evaluation showed that this method can significantly improve the prediction accuracy. As regards the problem of effectively visualizing the recommended resources and their relationships, this thesis proposes a visualization framework for DBpedia (the Linked Data version of Wikipedia) and mobile devices, which is designed to be extended to other datasets. In summary, this thesis shows how it is possible to exploit structured data available on the Web to recommend useful resources to users. Linked Data were successfully exploited in recommender systems. Various proposed approaches were implemented and applied to use cases of Telecom Italia

Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles

In micro-blogging platforms, people connect and interact with others. However, due to cognitive biases, they tend to interact with like-minded people and read agreeable information only. Many efforts to make people connect with those who think differently have not worked well. In this paper, we hypothesize, first, that previous approaches have not worked because they have been direct -- they have tried to explicitly connect people with those having opposing views on sensitive issues. Second, that neither recommendation or presentation of information by themselves are enough to encourage behavioral change. We propose a platform that mixes a recommender algorithm and a visualization-based user interface to explore recommendations. It recommends politically diverse profiles in terms of distance of latent topics, and displays those recommendations in a visual representation of each user's personal content. We performed an "in the wild" evaluation of this platform, and found that people explored more recommendations when using a biased algorithm instead of ours. In line with our hypothesis, we also found that the mixture of our recommender algorithm and our user interface, allowed politically interested users to exhibit an unbiased exploration of the recommended profiles. Finally, our results contribute insights in two aspects: first, which individual differences are important when designing platforms aimed at behavioral change; and second, which algorithms and user interfaces should be mixed to help users avoid cognitive mechanisms that lead to biased behavior.Comment: 12 pages, 7 figures. To be presented at ACM Intelligent User Interfaces 201