Recommending Structured Objects: Paths and Sets

Recommender systems have been widely adopted in industry to help people find the most appropriate items to purchase or consume from the increasingly large collection of available resources (e.g., books, songs and movies). Conventional recommendation techniques follow the approach of ``ranking all possible options and pick the top'', which can work effectively for single item recommendation but fall short when the item in question has internal structures. For example, a travel trajectory with a sequence of points-of-interest or a music playlist with a set of songs. Such structured objects pose critical challenges to recommender systems due to the intractability of ranking all possible candidates. This thesis study the problem of recommending structured objects, in particular, the recommendation of path (a sequence of unique elements) and set (a collection of distinct elements). We study the problem of recommending travel trajectories in a city, which is a typical instance of path recommendation. We propose methods that combine learning to rank and route planning techniques for efficient trajectory recommendation. Another contribution of this thesis is to develop the structured recommendation approach for path recommendation by substantially modifying the loss function, the learning and inference procedures of structured support vector machines. A novel application of path decoding techniques helps us achieve efficient learning and recommendation. Additionally, we investigate the problem of recommending a set of songs to form a playlist as an example of the set recommendation problem. We propose to jointly learn user representations by employing the multi-task learning paradigm, and a key result of equivalence between bipartite ranking and binary classification enables efficient learning of our set recommendation method. Extensive evaluations on real world datasets demonstrate the effectiveness of our proposed approaches for path and set recommendation

Differentially Private Trajectory Analysis for Points-of-Interest Recommendation

Ubiquitous deployment of low-cost mobile positioning devices and the widespread use of high-speed wireless networks enable massive collection of large-scale trajectory data of individuals moving on road networks. Trajectory data mining finds numerous applications including understanding users' historical travel preferences and recommending places of interest to new visitors. Privacy-preserving trajectory mining is an important and challenging problem as exposure of sensitive location information in the trajectories can directly invade the location privacy of the users associated with the trajectories. In this paper, we propose a differentially private trajectory analysis algorithm for points-of-interest recommendation to users that aims at maximizing the accuracy of the recommendation results while protecting the privacy of the exposed trajectories with differential privacy guarantees. Our algorithm first transforms the raw trajectory dataset into a bipartite graph with nodes representing the users and the points-of-interest and the edges representing the visits made by the users to the locations, and then extracts the association matrix representing the bipartite graph to inject carefully calibrated noise to meet ϵ-differential privacy guarantees. A post-processing of the perturbed association matrix is performed to suppress noise prior to performing a Hyperlink-Induced Topic Search (HITS) on the transformed data that generates an ordered list of recommended points-of-interest. Extensive experiments on a real trajectory dataset show that our algorithm is efficient, scalable and demonstrates high recommendation accuracy while meeting the required differential privacy guarantees

Privacy preserving path recommendation for moving user on location based service

With the increasing adoption of location based services, privacy is becoming a major concern. To hide the identity and location of a request on location based service, most methods consider a set of users in a reasonable region so as to confuse their requests. When there are not enough users, the cloaking region needs expanding to a larger area or the response needs delay. Either way degrades the quality-of-service. In this paper, we tackle the privacy problem in a predication way by recommending a privacy-preserving path for a requester. We consider the popular navigation application, where users may continuously query different location based servers during their movements. Based on a set of metrics on privacy, distance and the quality of services that a LBS requester often desires, a secure path is computed for each request according to user's preference, and can be dynamically adjusted when the situation is changed. A set of experiments are performed to verify our method and the relationship between parameters are discussed in details. We also discuss how to apply our method into practical applications. © 2013 IEEE.published_or_final_versio

Recommending places blased on the wisdom-of-the-crowd

The collective opinion of a great number of users, popularly known as wisdom of the crowd, has been seen as powerful tool for solving problems. As suggested by Surowiecki in his books [134], large groups of people are now considered smarter than an elite few, regardless of how brilliant at solving problems or coming to wise decisions they are. This phenomenon together with the availability of a huge amount of data on the Web has propitiated the development of solutions which employ the wisdom-of-the-crowd to solve a variety of problems in different domains, such as recommender systems [128], social networks [100] and combinatorial problems [152, 151]. The vast majority of data on the Web has been generated in the last few years by billions of users around the globe using their mobile devices and web applications, mainly on social networks. This information carries astonishing details of daily activities ranging from urban mobility and tourism behavior, to emotions and interests. The largest social network nowadays is Facebook, which in December 2015 had incredible 1.31 billion mobile active users, 4.5 billion “likes” generated daily. In addition, every 60 seconds 510 comments are posted, 293, 000 statuses are updated, and 136,000 photos are uploaded1. This flood of data has brought great opportunities to discover individual and collective preferences, and use this information to offer services to meet people’s needs, such as recommending relevant and interesting items (e.g. news, places, movies). Furthermore, it is now possible to exploit the experiences of groups of people as a collective behavior so as to augment the experience of other. This latter illustrates the important scenario where the discovery of collective behavioral patterns, the wisdom-of-the-crowd, may enrich the experience of individual users. In this light, this thesis has the objective of taking advantage of the wisdom of the crowd in order to better understand human mobility behavior so as to achieve the final purpose of supporting users (e.g. people) by providing intelligent and effective recommendations. We accomplish this objective by following three main lines of investigation as discussed below. In the first line of investigation we conduct a study of human mobility using the wisdom-of- the-crowd, culminating in the development of an analytical framework that offers a methodology to understand how the points of interest (PoIs) in a city are related to each other on the basis of the displacement of people. We experimented our methodology by using the PoI network topology to identify new classes of points of interest based on visiting patterns, spatial displacement from one PoI to another as well as popularity of the PoIs. Important relationships between PoIs are mined by discovering communities (groups) of PoIs that are closely related to each other based on user movements, where different analytical metrics are proposed to better understand such a perspective. The second line of investigation exploits the wisdom-of-the-crowd collected through user-generated content to recommend itineraries in tourist cities. To this end, we propose an unsupervised framework, called TripBuilder, that leverages large collections of Flickr photos, as the wisdom-of- the-crowd, and points of interest from Wikipedia in order to support tourists in planning their visits to the cities. We extensively experimented our framework using real data, thus demonstrating the effectiveness and efficiency of the proposal. Based on the theoretical framework, we designed and developed a platform encompassing the main features required to create personalized sightseeing tours. This platform has received significant interest within the research community, since it is recognized as crucial to understand the needs of tourists when they are planning a visit to a new city. Consequently this led to outstanding scientific results. In the third line of investigation, we exploit the wisdom-of-the-crowd to leverage recommendations of groups of people (e.g. friends) who can enjoy an item (e.g. restaurant) together. We propose GroupFinder to address the novel user-item group formation problem aimed at recommending the best group of friends for a pair. The proposal combines user-item relevance information with the user’s social network (ego network), while trying to balance the satisfaction of all the members of the group for the item with the intra-group relationships. Algorithmic solutions are proposed and experimented in the location-based recommendation domain by using four publicly available Location-Based Social Network (LBSN) datasets, showing that our solution is effective and outperforms strong baselines

A multiple criteria route recommendation system

The work to be developed in this dissertation is part of a larger project called Sustainable Tourism Crowding (STC), which motivation is based on two negative impacts caused by the tourism overload that happens, particularly, in the historic neighborhoods of Lisbon. The goal of this dissertation is then to mitigate those problems: reduce the tourist burden of points of interest in a city that, in addition to the degradation of the tourist experience, causes sustainability problems in different aspects (environmental, social and local). Within the scope of this dissertation, the implementation of one component of a recommendation system is the proposed solution. It is based on a multi-criteria algorithm for recommending pedestrian routes that minimize the passage through more crowded places and maximizes the visit to sustainable points of interest. These routes will be personalized for each user, as they consider their explicit preferences (e.g. time, budget, physical effort) and several constraints taken from other microservices that are part of the global system architecture mentioned above (e.g. weather conditions, crowding levels, points of interest, sustainability). We conclude it is possible to develop a microservice that recommend personalized routes and communicate with other microservices that are part of the global system architecture mentioned above. The analysis of the experimental data from the recommendation system, allows us to conclude that it is possible to obtain a more balanced distribution of the tourist visit, by increasing the visit to more sustainable places of interest and avoiding crowded paths.O trabalho a desenvolver nesta dissertação insere-se num projeto de maior dimensão denominado Sustainable Tourism Crowding (STC), cuja motivação assenta, essencialmente, em dois impactos negativos provocados pela sobrecarga turística que se verifica, nomeadamente, nos bairros históricos de Lisboa. O objetivo desta dissertação é, então, mitigar esses problemas: reduzir a sobrecarga turística dos pontos de interesse mais visitados numa cidade que, além da degradação da experiência turística, causa problemas de sustentabilidade em diversos aspetos (ambiental, social e local). No âmbito desta dissertação, a implementação de um componente de um sistema de recomendação é a solução proposta. Baseia-se num algoritmo multicritério de recomendação de percursos pedonais que minimiza a passagem por locais mais apinhados e maximizam a visita a pontos de interesse mais sustentáveis. Essas rotas serão personalizadas para cada utilizador, pois consideram as suas preferências (por exemplo, tempo, orçamento, nível de esforço físico) e várias restrições retiradas de outros microsserviços que fazem parte da arquitetura do sistema global mencionado acima (por exemplo, condições meteorológicas, níveis de apinhamento, pontos de interesse, níveis de sustentabilidade). Concluímos que é possível desenvolver um microsserviço que recomenda rotas personalizadas e que comunica com outros microsserviços que fazem parte da arquitetura global do sistema mencionada acima. A análise dos dados experimentais do sistema de recomendação, permite-nos concluir que é possível obter uma distribuição mais equilibrada da visita turística, aumentando a visita a pontos de interesse mais sustentáveis e evitando percursos mais apinhados