Towards personalized data-driven bundle design with QoS constraint

Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiativ

UniRecSys: A Unified Framework for Personalized, Group, Package, and Package-to-Group Recommendations

Recommender systems aim to enhance the overall user experience by providing tailored recommendations for a variety of products and services. These systems help users make more informed decisions, leading to greater user satisfaction with the platform. However, the implementation of these systems largely depends on the context, which can vary from recommending an item or package to a user or a group. This requires careful exploration of several models during the deployment, as there is no comprehensive and unified approach that deals with recommendations at different levels. Furthermore, these individual models must be closely attuned to their generated recommendations depending on the context to prevent significant variation in their generated recommendations. In this paper, we propose a novel unified recommendation framework that addresses all four recommendation tasks, namely personalized, group, package, or package-to-group recommendation, filling the gap in the current research landscape. The proposed framework can be integrated with most of the traditional matrix factorization-based collaborative filtering models. The idea is to enhance the formulation of the existing approaches by incorporating components focusing on the exploitation of the group and package latent factors. These components also help in exploiting a rich latent representation of the user/item by enforcing them to align closely with their corresponding group/package representation. We consider two prominent CF techniques, Regularized Matrix Factorization and Maximum Margin Matrix factorization, as the baseline models and demonstrate their customization to various recommendation tasks. Experiment results on two publicly available datasets are reported, comparing them to other baseline approaches that consider individual rating feedback for group or package recommendations.Comment: 25 page

Marketing of Tourism Destination in the Context of Tiger Safari

Tiger tourism plays a significant role in the overall scenario of Indian tourism. The forest destination managers face a major challenge in satisfying their visitors since tigers are elusive by nature and most of the time tourists return dissatisfied without sighting a tiger after a forest safari. This paper is the first scientific study of its kind based on empirical data in the context of tiger tourism and proposed a model to identify the optimum path in the forest with a higher probability of tiger sighting

VoyageWithUs : a recommender platform that enhances group travel planning

Group travel planning poses unique challenges such as choosing hotels, restaurants and venues while catering to everyone’s wants and needs, or sharing trip itineraries and artifacts among trip participants. State of the art travel planning applications such as Yelp and TripAdvisor, while integrating with social networks and making recommendations, don’t offer recommendations for specific groups of travelers. On the other hand, while TripCase offers trip planning capabilities and email sharing, it doesn’t offer a full interactive travel planner that allows groups to contribute to the travel planning process. This report proposes an approach to making personalized group travel recommendations based on hybrid recommendation techniques that aggregates individual recommendations to find common ground between trip participants. This is achieved by designing a recommender system that uses data from a location based social network(LBSN) and makes recommendations based on the trip location, then refines them by applying incremental filters which are responsible for incorporating user preferences, similarity to other users and user context. Finally, it takes the generated recommendations for each trip participant and ranks them such that the items most highly ranked are the ones most likely to fit everyone’s preferences. The rationale for choosing a hybrid recommender system is to address common issues such as the cold start problem, where the quality of the recommendations is affected by either too few reviewers for a certain point of interest(POI) or too few reviews generated by trip participants. These issues, along with a coverage of related work is detailed in the first part of this report. In order to make the applicability of the recommender more tangible, I integrated it into a proof of concept mobile application that also allows travelers to collaborate and share travel planning artifacts, and generates itineraries based on the recommendations made. The recommender accuracy was measured against recommendations made by state of the art applications, while individual filters were evaluated using commonly used metrics. The recommender was tested in a series of relevant scenarios proving the effectiveness of the approach in making group travel recommendations, versus individual recommendations generated by other applications.Electrical and Computer Engineerin

Beyond Traditional Software Development: Studying and Supporting the Role of Reusing Crowdsourced Knowledge in Software Development

As software development is becoming increasingly complex, developers often need to reuse others’ code or knowledge made available online to tackle problems encountered during software development and maintenance. This phenomenon of using others' code or knowledge, often found on online forums, is referred to as crowdsourcing. A good example of crowdsourcing is posting a coding question on the Stack Overflow website and having others contribute code that solves that question. Recently, the phenomenon of crowdsourcing has attracted much attention from researchers and practitioners and recent studies show that crowdsourcing improves productivity and reduces time-to-market. However, like any solution, crowdsourcing brings with it challenges such as quality, maintenance, and even legal issues. The research presented in this thesis presents the result of a series of large-scale empirical studies involving some of the most popular crowdsourcing platforms such as Stack Overflow, Node Package Manager (npm), and Python Package Index (PyPI). The focus of these empirical studies is to investigate the role of reusing crowdsourcing knowledge and more particularly crowd code in the software development process. We first present two empirical studies on the reuse of knowledge from crowdsourcing platforms namely Stack Overflow. We found that reusing knowledge from this crowdsourcing platform has the potential to assist software development practices, specifically through source code reuse. However, relying on such crowdsourced knowledge might also negatively affect the quality of the software projects. Second, we empirically examine the type of development knowledge constructed on crowdsourcing platforms. We examine the use of trivial packages on npm and PyPI platforms. We found that trivial packages are common and developers tend to use them because they provide them with well tested and implemented code. However, developers are concerned about the maintenance overhead of these trivial packages due to the extra dependencies that trivial packages introduce. Finally, we used the gained knowledge to propose a pragmatic solution to improve the efficiency of relying on the crowd in software development. We proposed a rule-based technique that automatically detects commits that can skip the continuous integration process. We evaluate the performance of the proposed technique on a dataset of open-source Java projects. Our results show that continuous integration can be used to improve the efficiency of the reused code from crowdsourcing platforms. Among the findings of this thesis are that the way software is developed has changed dramatically. Developers rely on crowdsourcing to address problems encountered during software development and maintenance. The results presented in this thesis provides new insights on how knowledge from these crowdsourced platforms is reused in software systems and how some of this knowledge can be better integrated into current software development processes and best practices

A context aware recommender system for tourism with ambient intelligence

Recommender system (RS) holds a significant place in the area of the tourism sector. The major factor of trip planning is selecting relevant Points of Interest (PoI) from tourism domain. The RS system supposed to collect information from user behaviors, personality, preferences and other contextual information. This work is mainly focused on user’s personality, preferences and analyzing user psychological traits. The work is intended to improve the user profile modeling, exposing relationship between user personality and PoI categories and find the solution in constraint satisfaction programming (CSP). It is proposed the architecture according to ambient intelligence perspective to allow the best possible tourist place to the end-user. The key development of this RS is representing the model in CSP and optimizing the problem. We implemented our system in Minizinc solver with domain restrictions represented by user preferences. The CSP allowed user preferences to guide the system toward finding the optimal solutions; RESUMO O sistema de recomendação (RS) detém um lugar significativo na área do sector do turismo. O principal fator do planeamento de viagens é selecionar pontos de interesse relevantes (PoI) do domínio do turismo. O sistema de recomendação (SR) deve recolher informações de comportamentos, personalidade, preferências e outras informações contextuais do utilizador. Este trabalho centra-se principalmente na personalidade, preferências do utilizador e na análise de traços fisiológicos do utilizador. O trabalho tem como objetivo melhorar a modelação do perfil do utilizador, expondo a relação entre a personalidade deste e as categorias dos POI, assim como encontrar uma solução com programação por restrições (CSP). Propõe-se a arquitetura de acordo com a perspetiva do ambiente inteligente para conseguir o melhor lugar turístico possível para o utilizador final. A principal contribuição deste SR é representar o modelo como CSP e tratá-lo como problema de otimização. Implementámos o nosso sistema com o solucionador em Minizinc com restrições de domínio representadas pelas preferências dos utilizadores. O CSP permitiu que as preferências dos utilizadores guiassem o sistema para encontrar as soluções ideais

Mobile Crowdsensing και Εφαρμογές

Η ραγδαία ανάπτυξη της τεχνολογίας έχει οδηγήσει, μεταξύ άλλων, και στη ραγδαία αύξηση των ατόμων που κατέχουν έξυπνα κινητά και συσκευές, εξοπλισμένα με αισθητήρες και ισχυρούς επεξεργαστές. Κύριο αντικείμενο αυτής της εργασίας είναι η μελέτη των δυνατοτήτων που προσφέρουν τα δεδομένα που συλλέγονται από τους αισθητήρες αυτούς, καθώς και οι πληροφορίες που προκύπτουν από την περαιτέρω επεξεργασία τους. Η διαδικασία αυτή καλείται Mobile Crowdsensing, ονομασία που προέρχεται από τις λέξεις crowd (πλήθος) και sensing (αίσθηση). Στη συνέχεια, μελετούμε διαφορετικούς τρόπους με τους οποίους μπορεί να πραγματοποιείται η ροή των δεδομένων, από ποια στάδια αποτελείται ο κύκλος λειτουργίας μιας Mobile Crowdsensing εφαρμογής, καθώς και ποιες είναι οι προκλήσεις που καλούμαστε να αντιμετωπίσουμε. Αυτές εστιάζονται κυρίως στην διασφάλιση της ιδιωτικότητας του χρήστη και στην διατήρηση χαμηλών επιπέδων κατανάλωσης ενέργειας και πόρων στην κινητή συσκευή του. Τέλος αναλύουμε τρία χαρακτηριστικά παραδείγματα Mobile Crowdsensing εφαρμογών, δηλαδή τον τρόπο λειτουργίας τους, πως αντιμετωπίστηκαν οι προκλήσεις και με ποιο τρόπο οι πληροφορίες που τελικά προκύπτουν μπορούν να φανούν χρήσιμες για την εξαγωγή συμπερασμάτων.Τhe rapid proliferation of smartphones and the increasing availability of sensors on everyday devices, carried around by millions of people, has opened up diverse kinds of information gathering. In this thesis we investigate the the possibility of harvesting large quantities of data in urban areas exploiting user devices, thus enabling the so-called Mobile Crowdsensing (MCS). Human involvement is one of the most important characteristics of MCS, so we further investigate the opportunistic characteristics of human mobility from the perspectives of both sensing and transmission. Then, we examine a four-stage life cycle to characterize the MCS process and discuss the challenges that occur, as far as the dimensions of trust and privacy and of battery consumption are concerned. Furthermore, we describe and analyse three representative MCS applications, i.e. the system architecture, how the above challenges were overcome and what could the use of the collected data and information be