Location-aware recommendation systems: Where we are and where we recommend to go

Recommendation systems have been successfully used to provide items of interest to the users (e.g., movies, music, books, news, images). However, traditional recommenda- tion systems do not take into account the location as a relevant factor when providing suggestions. On the other hand, nowadays, there exist an increasing amount of geo- referenced data and users are usually interested only in nearby items (e.g., restaurants, museums, cinemas). Hence, the emergence of location-aware recommendation systems have acquired a great attention by the research community in the last decade. In this paper, we provide a survey of location-aware rec- ommendation systems in mobile computing scenarios. Firstly, we describe briefly the fundamentals of recommendation sys- tems. Then, we introduce some of the most relevant existing approaches for location-aware recommendation. Moreover, we present the main applications of this type of systems in several recommendation scenarios, such as music, news, restaurants, etc. Finally, we discuss new avenues and open issues in the area

On effective location-aware music recommendation

Ministry of Education, Singapore under its Academic Research Funding Tier

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

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

Context-Aware Recommender Systems for Implicit Data

Recommender systems are software tools and techniques providing suggestions and recommendations for items to be of use to a user. These sug- gestions can help users make better decisions on choosing products or services, such as which film to watch, what music to listen to or which travel insurance to buy. When making suggestions, many recommender systems do not consider contextual information, such as location or time [5]. Recommender systems that make use of contextual information are called context-aware recommender systems. Many context-aware recommender systems can not generate reliable rec- ommendations on sparse data. Besides, in most context-aware recommender systems, the contexts are pre-defined and not personalised. These limitations of existing methods usually lead to inaccurate recommendations. In this thesis, new context-aware recommendation methods are presented. In these methods, personalised contexts are defined based on users’ activity patterns. The underlying associations between contexts are analysed, and similar contexts are combined so that the system can make use of existing data collected in similar contexts. Experimental results from two datasets show that the proposed methods can achieve significantly higher recommendation accuracy than existing context-aware recommendation methods

Just-for-Me: An Adaptive Personalization System for Location-Aware Social Music Recommendation

The fast growth of online communities and increasing pop-ularity of internet-accessing smart devices have significantly changed the way people consume and share music. As an emerging technology to facilitate effective music retrieval on the move, intelligent recommendation has been recently re-ceived great attentions in recent years. While a large amount of efforts have been invested in the field, the technology is still in its infancy. One of the major reasons for this stagna-tion is due to inability of the existing approaches to compre-hensively take multiple kinds of contextual information into account. In the paper, we present a novel recommender sys-tem called Just-for-Me to facilitate effective social music rec-ommendation by considering users ’ location related contexts as well as global music popularity trends. We also develop an unified recommendation model to integrate the contex-tual factors as well as music contents simultaneously. Fur-thermore, pseudo-observations are proposed to overcome the cold-start and sparsity problems. An extensive experimental study based on different test collections demonstrates that Just-for-Me system can significantly improve the recommen-dation performance at various geo-locations

Travel recommendations in a mobile tourist information system

An advanced mobile tourist information system delivers information about sights and events on a tourists travel route. The system should be personalized in its interaction with the tourist. Data that can be used for personalization are: the tourists interest profile, an analysis of their travel history, and the tourists feedback about sights. Existing mobile information systems for tourists do not tailor their information delivery to the tourists interests. In this paper, we propose the use of personalised recommendations that consider all of the personal information a tourist provides. We adopt and modify techniques from recommended systems to the new application area of mobile tourist information. We propose a number of methods for personalised recommendations; and select a subset of these for implementation. This paper then presents the implemented recommended component of our TIP system for mobile tourist informatio