327 research outputs found
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
Exploring attributes, sequences, and time in Recommender Systems: From classical to Point-of-Interest recommendation
Tesis Doctoral inédita leÃda en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingenieria Informática. Fecha de lectura: 08-07-2021Since the emergence of the Internet and the spread of digital communications
throughout the world, the amount of data stored on the Web has been
growing exponentially. In this new digital era, a large number of companies
have emerged with the purpose of ltering the information available on the
web and provide users with interesting items. The algorithms and models
used to recommend these items are called Recommender Systems. These
systems are applied to a large number of domains, from music, books, or
movies to dating or Point-of-Interest (POI), which is an increasingly popular
domain where users receive recommendations of di erent places when
they arrive to a city.
In this thesis, we focus on exploiting the use of contextual information, especially
temporal and sequential data, and apply it in novel ways in both
traditional and Point-of-Interest recommendation. We believe that this type
of information can be used not only for creating new recommendation models
but also for developing new metrics for analyzing the quality of these
recommendations. In one of our rst contributions we propose di erent
metrics, some of them derived from previously existing frameworks, using
this contextual information. Besides, we also propose an intuitive algorithm
that is able to provide recommendations to a target user by exploiting the
last common interactions with other similar users of the system.
At the same time, we conduct a comprehensive review of the algorithms
that have been proposed in the area of POI recommendation between 2011
and 2019, identifying the common characteristics and methodologies used.
Once this classi cation of the algorithms proposed to date is completed, we
design a mechanism to recommend complete routes (not only independent
POIs) to users, making use of reranking techniques. In addition, due to the
great di culty of making recommendations in the POI domain, we propose
the use of data aggregation techniques to use information from di erent
cities to generate POI recommendations in a given target city.
In the experimental work we present our approaches on di erent datasets
belonging to both classical and POI recommendation. The results obtained
in these experiments con rm the usefulness of our recommendation proposals,
in terms of ranking accuracy and other dimensions like novelty, diversity,
and coverage, and the appropriateness of our metrics for analyzing temporal
information and biases in the recommendations producedDesde la aparici on de Internet y la difusi on de las redes de comunicaciones
en todo el mundo, la cantidad de datos almacenados en la red ha crecido
exponencialmente. En esta nueva era digital, han surgido un gran n umero
de empresas con el objetivo de ltrar la informaci on disponible en la red
y ofrecer a los usuarios art culos interesantes. Los algoritmos y modelos
utilizados para recomendar estos art culos reciben el nombre de Sistemas de
Recomendaci on. Estos sistemas se aplican a un gran n umero de dominios,
desde m usica, libros o pel culas hasta las citas o los Puntos de Inter es (POIs,
en ingl es), un dominio cada vez m as popular en el que los usuarios reciben
recomendaciones de diferentes lugares cuando llegan a una ciudad.
En esta tesis, nos centramos en explotar el uso de la informaci on contextual,
especialmente los datos temporales y secuenciales, y aplicarla de forma novedosa
tanto en la recomendaci on cl asica como en la recomendaci on de POIs.
Creemos que este tipo de informaci on puede utilizarse no s olo para crear
nuevos modelos de recomendaci on, sino tambi en para desarrollar nuevas
m etricas para analizar la calidad de estas recomendaciones. En una de
nuestras primeras contribuciones proponemos diferentes m etricas, algunas
derivadas de formulaciones previamente existentes, utilizando esta informaci
on contextual. Adem as, proponemos un algoritmo intuitivo que es
capaz de proporcionar recomendaciones a un usuario objetivo explotando
las ultimas interacciones comunes con otros usuarios similares del sistema.
Al mismo tiempo, realizamos una revisi on exhaustiva de los algoritmos que
se han propuesto en el a mbito de la recomendaci o n de POIs entre 2011 y
2019, identi cando las caracter sticas comunes y las metodolog as utilizadas.
Una vez realizada esta clasi caci on de los algoritmos propuestos hasta la
fecha, dise~namos un mecanismo para recomendar rutas completas (no s olo
POIs independientes) a los usuarios, haciendo uso de t ecnicas de reranking.
Adem as, debido a la gran di cultad de realizar recomendaciones en el
ambito de los POIs, proponemos el uso de t ecnicas de agregaci on de datos
para utilizar la informaci on de diferentes ciudades y generar recomendaciones
de POIs en una determinada ciudad objetivo.
En el trabajo experimental presentamos nuestros m etodos en diferentes
conjuntos de datos tanto de recomendaci on cl asica como de POIs. Los
resultados obtenidos en estos experimentos con rman la utilidad de nuestras
propuestas de recomendaci on en t erminos de precisi on de ranking y de
otras dimensiones como la novedad, la diversidad y la cobertura, y c omo de
apropiadas son nuestras m etricas para analizar la informaci on temporal y
los sesgos en las recomendaciones producida
Analyzing and predicting the spatial penetration of Airbnb in U.S. cities
In the hospitality industry, the room and apartment sharing platform of Airbnb has been accused of unfair competition. Detractors have pointed out the chronic lack of proper legislation. Unfortunately, there is little quantitative evidence about Airbnb's spatial penetration upon which to base such a legislation. In this study, we analyze Airbnb's spatial distribution in eight U.S. urban areas, in relation to both geographic, socio-demographic, and economic information. We find that, despite being very different in terms of population composition, size, and wealth, all eight cities exhibit the same pattern: that is, areas of high Airbnb presence are those occupied by the \newpart{``talented and creative''} classes, and those that are close to city centers. This result is consistent so much so that the accuracy of predicting Airbnb's spatial penetration is as high as 0.725
Development of Context-Aware Recommenders of Sequences of Touristic Activities
En els últims anys, els sistemes de recomanació s'han fet omnipresents a la xarxa. Molts serveis web, inclosa la transmissió de pel·lÃcules, la cerca web i el comerç electrònic, utilitzen sistemes de recomanació per facilitar la presa de decisions. El turisme és una indústria molt representada a la xarxa. Hi ha diversos serveis web (e.g. TripAdvisor, Yelp) que es beneficien de la integració de sistemes recomanadors per ajudar els turistes a explorar destinacions turÃstiques. Això ha augmentat la investigació centrada en la millora dels recomanadors turÃstics per resoldre els principals problemes als quals s'enfronten. Aquesta tesi proposa nous algorismes per a sistemes recomanadors turÃstics que aprenen les preferències dels turistes a partir dels seus missatges a les xarxes socials per suggerir una seqüència d'activitats turÃstiques que s'ajustin a diversos contextes i incloguin activitats afins. Per aconseguir-ho, proposem mètodes per identificar els turistes a partir de les seves publicacions a Twitter, identificant les activitats experimentades en aquestes publicacions i perfilant turistes similars en funció dels seus interessos, informació contextual i perÃodes d'activitat. Aleshores, els perfils d'usuari es combinen amb un algorisme de mineria de regles d'associació per capturar relacions implÃcites entre els punts d'interès de cada perfil. Finalment, es fa un rà nquing de regles i un procés de selecció d'un conjunt d'activitats recomanables. Es va avaluar la precisió de les recomanacions i l'efecte del perfil d'usuari. A més, ordenem el conjunt d'activitats mitjançant un algorisme multi-objectiu per enriquir l'experiència turÃstica. També realitzem una segona fase d'anà lisi dels fluxos turÃstics a les destinacions que és beneficiós per a les organitzacions de gestió de destinacions, que volen entendre la mobilitat turÃstica. En general, els mètodes i algorismes proposats en aquesta tesi es mostren útils en diversos aspectes dels sistemes de recomanació turÃstica.En los últimos años, los sistemas de recomendación se han vuelto omnipresentes en la web. Muchos servicios web, incluida la transmisión de pelÃculas, la búsqueda en la web y el comercio electrónico, utilizan sistemas de recomendación para ayudar a la toma de decisiones. El turismo es una industria altament representada en la web. Hay varios servicios web (e.g. TripAdvisor, Yelp) que se benefician de la inclusión de sistemas recomendadores para ayudar a los turistas a explorar destinos turÃsticos. Esto ha aumentado la investigación centrada en mejorar los recomendadores turÃsticos y resolver los principales problemas a los que se enfrentan. Esta tesis propone nuevos algoritmos para sistemas recomendadores turÃsticos que aprenden las preferencias de los turistas a partir de sus mensajes en redes sociales para sugerir una secuencia de actividades turÃsticas que se alinean con diversos contextos e incluyen actividades afines. Para lograr esto, proponemos métodos para identificar a los turistas a partir de sus publicaciones en Twitter, identificar las actividades experimentadas en estas publicaciones y perfilar turistas similares en función de sus intereses, contexto información y periodos de actividad. Luego, los perfiles de usuario se combinan con un algoritmo de minerÃa de reglas de asociación para capturar relaciones entre los puntos de interés que aparecen en cada perfil. Finalmente, un proceso de clasificación de reglas y selección de actividades produce un conjunto de actividades recomendables. Se evaluó la precisión de las recomendaciones y el efecto de la elaboración de perfiles de usuario. Ordenamos además el conjunto de actividades utilizando un algoritmo multi-objetivo para enriquecer la experiencia turÃstica. También llevamos a cabo un análisis de los flujos turÃsticos en los destinos, lo que es beneficioso para las organizaciones de gestión de destinos, que buscan entender la movilidad turÃstica. En general, los métodos y algoritmos propuestos en esta tesis se muestran útiles en varios aspectos de los sistemas de recomendación turÃstica.In recent years, recommender systems have become ubiquitous on the web. Many web services, including movie streaming, web search and e-commerce, use recommender systems to aid human decision-making. Tourism is one industry that is highly represented on the web. There are several web services (e.g. TripAdvisor, Yelp) that benefit from integrating recommender systems to aid tourists in exploring tourism destinations. This has increased research focused on improving tourism recommender systems and solving the main issues they face. This thesis proposes new algorithms for tourism recommender systems that learn tourist preferences from their social media data to suggest a sequence of touristic activities that align with various contexts and include affine activities. To accomplish this, we propose methods for identifying tourists from their frequent Twitter posts, identifying the activities experienced in these posts, and profiling similar tourists based on their interests, contextual information, and activity periods. User profiles are then combined with an association rule mining algorithm for capturing implicit relationships between points of interest apparent in each profile. Finally, a rule ranking and activity selection process produces a set of recommendable activities. The recommendations were evaluated for accuracy and the effect of user profiling. We further order the set of activities using a multi-objective algorithm to enrich the tourist experience. We also carry out a second-stage analysis of tourist flows at destinations which is beneficial to destination management organisations
seeking to understand tourist mobility. Overall, the methods and algorithms proposed in this thesis are shown to be useful in various aspects of tourism recommender systems
Context-Aware Recommendation Systems in Mobile Environments
Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /
The feeding ecology of extralimital Nyala (Tragelaphus Angasii) in the Arid Mosaic thicket of the Southern Cape
The nyala on Buffelsdrift Private Game Reserve were found to be mainly browsers and made little use of grass during the study period. Browse contributed 87.8 percent to the annual diet, while grass contributed 12.2 percent. Only six species formed > 5 percent of the annual diet. Of these six only three species formed > 10 percent. They are the shrubs Acacia karroo, Pappea capensis and Grewia robusta. These were followed by the shrub Portulacaria afra, the dwarf shrub Zygophyllum lichtensteinianum and the grass Cynodon dactylon all contributing between 5 - 10 percent. There were definite seasonal dietary shifts with preference to different plant species. The shift takes place between the wet seasons (winter and spring) and the dry season (summer). Herbaceous species such as Cynodon dactylon and Erharta calycina were favoured in the winter/wet season. Shrubs such as P. capensis, P. afra and dwarf shrubs such as Limeum aethiopicum and Z. lichtensteinianum were favoured during the spring/wet and summer/dry seasons. Nyala habitat use in BPGR differed significantly from expected use when taking the relative area of habitats into consideration. However nyala did not display signififcantly different habitat preferences between seasons. Male and female nyala displayed differences in foraging behaviour at the plant form, plant species and habitat scale. Males preferred a greater proportion of shrubs and females a greater proportion of dwarf shrubs. Males preferred the shrubs Pappea capensis and Portulacaria afra the most and females preferred the dwarf shrubs Limeum aethiopicum and Zygophyllum lichtensteinianum the most. Plant species site availability varied significantly between the sexes, indicating that males and females selected different feeding paths. The difference in their feeding strategy appears to be related to the selection of plants that best satisfy the nutrient requirements of each sex. These findings are in agreement with the results from earlier studies on nyala sexual dimorphism. At the habitat scale females selected more for open dwarf shrubland and males more for densely vegetated habitats. This appears to be as a result of males attempting to maximize their foraging opportunities by selecting areas that offer food in greater quantities. Nyala in BPGR appear to co-exist with kudu and eland through spatial resource partitioning. This is suggested to be due to the influence of body size-gut size relationships on forage selectivity
LIPIcs, Volume 277, GIScience 2023, Complete Volume
LIPIcs, Volume 277, GIScience 2023, Complete Volum
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