Time aware location recommendations in location based social networks

Smartphones have fundamentally changed how people make choices about the products and services they consume and the way they interact with each other. The spread and extensive usage of mobile applications has led to the rise of Location-based Social Network (LBSN) services like Foursquare, Yelp etc, that aim to aim to provide new and novel places of interest to people based on their interests and habits. A Recommendation system helps users to discover places they may like and also enable them to narrow down their choices. In particular, providing relevant location recommendations to users is essential to drive customer engagement with the mobile application and is also an important research topic. Multiple studies on human mobility patterns and my analysis on different LBSN datasets have shown that the preference of users for different locations changes with time, i.e., type of locations visited in the afternoon are different from those visited in the evening. Majority of recommendation systems in LBSN do not take into account the temporal aspect of recommendation. A recommendation system must be able to provide locations to users by taking into account their 1) stationary preferences that don't change with time and 2) temporal preference that differ with time and recommended locations that are relevant in time to the user. This thesis first presents a feature based location recommendation model, REGULA that exploits the regular mobility behavior of people and incorporates temporal information to provide better location recommendations. REGULA outperforms other feature and graph-based location recommendation models. Further, this thesis presents the first model to recommend interesting areas to people based on their Call Detail Records. Finally, this thesis presents two deep neural network based location recommendation models ( DEEPREC and DEEPTREC ) that are used to learn the stationary and temporal preferences of users. The combined model ( JOINTDEEPREC ) can be used to provide time-aware location recommendations to people. The model was evaluated on one of the largest check-in dataset collected at Microsoft Research Asia and outperforms state-of-the-art model by a factor of 10

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

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

Hikester - the event management application

Today social networks and services are one of the most important part of our everyday life. Most of the daily activities, such as communicating with friends, reading news or dating is usually done using social networks. However, there are activities for which social networks do not yet provide adequate support. This paper focuses on event management and introduces "Hikester". The main objective of this service is to provide users with the possibility to create any event they desire and to invite other users. "Hikester" supports the creation and management of events like attendance of football matches, quest rooms, shared train rides or visit of museums in foreign countries. Here we discuss the project architecture as well as the detailed implementation of the system components: the recommender system, the spam recognition service and the parameters optimizer

Hoodsquare: Modeling and Recommending Neighborhoods in Location-based Social Networks

Information garnered from activity on location-based social networks can be harnessed to characterize urban spaces and organize them into neighborhoods. In this work, we adopt a data-driven approach to the identification and modeling of urban neighborhoods using location-based social networks. We represent geographic points in the city using spatio-temporal information about Foursquare user check-ins and semantic information about places, with the goal of developing features to input into a novel neighborhood detection algorithm. The algorithm first employs a similarity metric that assesses the homogeneity of a geographic area, and then with a simple mechanism of geographic navigation, it detects the boundaries of a city's neighborhoods. The models and algorithms devised are subsequently integrated into a publicly available, map-based tool named Hoodsquare that allows users to explore activities and neighborhoods in cities around the world. Finally, we evaluate Hoodsquare in the context of a recommendation application where user profiles are matched to urban neighborhoods. By comparing with a number of baselines, we demonstrate how Hoodsquare can be used to accurately predict the home neighborhood of Twitter users. We also show that we are able to suggest neighborhoods geographically constrained in size, a desirable property in mobile recommendation scenarios for which geographical precision is key.Comment: ASE/IEEE SocialCom 201