1,979 research outputs found
Travel Package Recommendation
Location Based SocialNetworks (LBSN) benefit the users by allowing them to share their locations and life
moments with their friends. The users can also review the locations they have visited. Classical recommender
systems provide users a ranked list of single items. This is not suitable for applications like trip
planning,where the recommendations should contain multiple items in an appropriate sequence. The
problem of generating such recommendations is challenging due to various critical aspects, which includes
user interest, budget constraints and high sparsity in the available data used to solve the problem.
In this paper, we propose a graph based approach to recommend a set of personalized travel packages.
Each recommended package comprises of a sequence of multiple Point of Interests (POIs). Given the current
location and spatio-temporal constraints, our goal is to recommend a package which satisfies the
constraints. This approach utilizes the data collected fromLBSNs to learn user preferences and also models
the location popularity
Design and Implementation of Recommendation Engine Based Suggestions for E-Tourism Application
Tourism is a largest service industry in India. When a user�s are interested in visiting the places, he searches the information about particular places in the internet. So to get appropriate information about a particular spot, the websites shows same data to all the users. So searching for the particular place and getting amount of information which is out of interest of user which is waste of time. Tourism is effective and efficient when the data which is provided should be according to what user demanding or expecting, instead of providing a huge amount of information. Service provided which should fulfill user demands
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
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
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
A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data
Tourism is an important application domain for recommender systems. In this
domain, recommender systems are for example tasked with providing personalized
recommendations for transportation, accommodation, points-of-interest (POIs),
or tourism services. Among these tasks, in particular the problem of
recommending POIs that are of likely interest to individual tourists has gained
growing attention in recent years. Providing POI recommendations to tourists
\emph{during their trip} can however be especially challenging due to the
variability of the users' context. With the rapid development of the Web and
today's multitude of online services, vast amounts of data from various sources
have become available, and these heterogeneous data sources represent a huge
potential to better address the challenges of in-trip POI recommendation
problems. In this work, we provide a comprehensive survey of published research
on POI recommendation between 2017 and 2022 from the perspective of
heterogeneous data sources. Specifically, we investigate which types of data
are used in the literature and which technical approaches and evaluation
methods are predominant. Among other aspects, we find that today's research
works often focus on a narrow range of data sources, leaving great potential
for future works that better utilize heterogeneous data sources and diverse
data types for improved in-trip recommendations.Comment: 35 pages, 19 figure
Tour recommendation for groups
Consider a group of people who are visiting a major touristic city, such as NY, Paris, or Rome. It is reasonable to assume that each member of the group has his or her own interests or preferences about places to visit, which in general may differ from those of other members. Still, people almost always want to hang out together and so the following question naturally arises: What is the best tour that the group could perform together in the city? This problem underpins several challenges, ranging from understanding people’s expected attitudes towards potential points of interest, to modeling and providing good and viable solutions. Formulating this problem is challenging because of multiple competing objectives. For example, making the entire group as happy as possible in general conflicts with the objective that no member becomes disappointed. In this paper, we address the algorithmic implications of the above problem, by providing various formulations that take into account the overall group as well as the individual satisfaction and the length of the tour. We then study the computational complexity of these formulations, we provide effective and efficient practical algorithms, and, finally, we evaluate them on datasets constructed from real city data
Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey
open access articleCollaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems
An Intelligent Customization Framework for Tourist Trip Design Problems
In the era of the experience economy, “customized tours” and “self-guided tours” have become mainstream. This paper proposes an end-to-end framework for solving the tourist trip design problems (TTDP) using deep reinforcement learning (DRL) and data analysis. The proposed approach considers heterogeneous tourist preferences, customized requirements, and stochastic traffic times in real applications. With various heuristics methods, our approach is scalable without retraining for every new problem instance, which can automatically adapt the solution when the problem constraint changes slightly. We aim to provide websites or users with software tools that make it easier to solve TTDP, promoting the development of smart tourism and customized tourism
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