1,720,986 research outputs found
Classifying and mapping e-Tourism data sets
Full text linkHeutzutage kann die Online-Recherche vor der Buchung eines Urlaubs als übliche Ge- wohnheit der Kunden angesehen werden. In diesem Zusammenhang zielen Recommender Systeme darauf ab, die Kunden bei ihrer Suche nach den richtigen Produkten zu unterstüt- zen. Jedoch stehen solche Systeme domänenspezifischen Herausforderungen gegenüber, da Tourismusprodukte typischerweise sehr komplex und mit Emotionen verbunden sind. Um diesen Herausforderungen entgegen zu treten, wurden umfassende Benutzermodelle entwickelt, welche die Präferenzen, die Anforderungen und die Persönlichkeit von Kunden berücksichtigen. Eines dieser Modelle ist das sogenannte Sieben-Faktoren-Modell. In dieser Arbeit werden verschiedene Methoden zur automatisierten Bestimmung der Sieben- Faktoren von Tourismusdestinationen und Hotels untersucht, um Recommender Systeme zu ermöglichen die passendsten Produkte vorzuschlagen. Insbesondere werden explorative Datenanalysen, Clusteranalysen und Regressionsanalysen durchgeführt, um nicht nur die Sieben-Faktoren von Tourismusdestinationen und Hotels zu bestimmen, sondern auch ausschlaggebende Attribute von Tourismusdestinationen und Hotels zu identifizieren. Die Resultate der Clusteranalysen zeigen, dass ähnliche Tourismusdestinationen und auch ähnliche Hotels gruppiert werden können. Die identifizierten Gruppen können mit den Sieben-Faktoren assoziiert werden. Die Ergebnisse der Clusteranalysen ermöglichen es nicht einzelne Faktoren des Sieben-Faktoren-Modells zu bestimmen, aber können für eine direkte Zuordnung verwendet werden. Im Gegensatz zu den Clusteranalysen liefern die Regressionsanalysen einen klaren Beweis dafür, dass die Sieben-Faktoren von Tourismusdestinationen und Hotels unter Berücksichtigung der jeweiligen Attribute bestimmt werden können. Grundsätzlich variiert die Qualität der entwickelten Modelle für verschiedene Faktoren des Sieben-Faktoren-Modells und auch für verschiedene Touris- musprodukte (Destination und Hotels). Der in dieser Arbeit vorgestellte Ansatz kann für neue Datenquellen und auch Produkttypen leicht nachvollzogen werden.Nowadays, researching online before booking a vacation can be seen as a common habit of customers. In this context, Recommender Systems (RSs) are aiming to support the customers to find the right products, but they face domain specific challenges since tourism products are typically very complex and related to emotional experiences. To counteract these challenges, comprehensive user models for capturing the preferences and personality of travelers have been introduced. One of these models is the so-called Seven-Factor Model. This work introduces an automated way for determining the Seven- Factor representation of tourism destinations and hotels to enable a matchmaking for RSs. In particular, exploratory data analyses, cluster analyses, and regression analyses are conducted not only to find a mapping of tourism destinations and hotels onto the Seven- Factors, but also to foster a better understanding of the relationship between destination attributes and the Seven-Factors, and between hotel attributes and the Seven-Factors. The main results show that conceptually meaningful groups of destinations and hotels as well can be identified and associated with the Seven-Factors, but they can only be used for direct allocations rather than for determining each factor of the Seven-Factor Model. Furthermore, the regression analyses provide clear evidence that a tourism destination’s Seven-Factor representation and a hotel’s Seven-Factor representation can be determined by taking the respective attributes into account. In general, the quality of the developed models varies for different factors of the Seven-Factor Model and also for different tourism products (i.e., destination and hotels). Finally, the introduced approach can easily be followed for new data sources and product types
Leveraging the Subtle: Hidden Factors in Recommender Systems
Full text linkRecommender systems are pivotal in various domains, aiding users in their decision-making. However, current systems often overlook subtle factors that significantly impact user preferences and choices. This work aims to bridge this gap by exploring the conceptof implicit item characteristics -- latent features that influence user decision-making in addition to explicit content. The investigation is divided into three key research areas. Firstly, we explore how to systematically identify and expose implicit item characteristics to enhance recommender systems in two key domains: tourism and news. Using advanced analytics such as cluster analysis and multiple linear regression, we map tourist destinations to the established Seven-Factor Model in tourism. In the news, we employ natural language processing techniques to reveal hidden features essential for tailoring recommendations. Secondly, we introduce a novel system called PicTouRe to elicit tourists' implicit preferences through pictures. Leveraging convolutional neural networks, we translate visual preferences into a Seven-Factor profile for each user, simplifying decision-making and capturing both immediate touristic desires and enduring personality traits. Lastly, we enhance news recommender systems by leveraging sentiment and emotions of news articles. Two models, RobustSentiRec and EmoRec, were developed to capture these implicit characteristics, aligning recommendations more closely with user preferences but also raising ethical concerns around potential sentiment and emotional echo chambers. Our findings offer a robust framework for more nuanced, user-sensitive recommendations, opening new avenues for future research and applications in recommender systems
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Exploring Expressed Emotions for Neural News Recommendation
No full textDue to domain-specific challenges such as short item lifetimes and continuous cold-start issues, news recommender systems rely more on content-based methods to deduce reliable user models and make personalized recommendations. Research has shown that alongside the content of an item, the way it is presented to the users also plays a critical role. In this work, we focus on the effect of incorporating expressed emotions within news articles on recommendation performance. We propose a neural news recommendation model that disentangles semantic and emotional modeling of news articles and users. While we exploit the textual content for the semantic representation, we extract and combine emotions of different information levels for the emotional representation. Offline experiments on a real-world dataset show that our approach outperforms non-emotion-aware solutions significantly. Finally, we provide a future outline, where we plan to investigate a) the online performance and b) the explainability/explorability of our approach
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