scholarly journals Bound information metric Fisher

2021 ◽  
Author(s):  
Ku Vit
Keyword(s):  

Bound information metric fisher

Author(s):  
Lingling Pu ◽  
Michael W. Marcellin ◽  
Ali Bilgin ◽  
Amit Ashok

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ruggero Sainaghi ◽  
Rodolfo Baggio

Purpose This paper aims to examine the question of whether commercial, peer-to-peer accommodation platforms (Airbnb, in particular) and hotels are in fierce competition with each other with the possible presence of substitution threats, and compares the time series of the occupancy values across two supplier types. Design/methodology/approach The cities of Milan and Rome are used as case studies for this analysis. To assess the extent of synchronization, the series of Airbnb and hotels are transformed into a series of symbols that render their rhythmic behavior, and a mutual information metric is used to measure the effect. Findings The results show that Airbnb hosts and hotels have different seasonal patterns. The diverse occupancy trends support the absence of direct competition between Airbnb and hotels. The findings are consistent in the two analyzed cities (Milan and Rome). Interestingly, there are higher similarities between seasonal occupancy series of Airbnb listings in Milan and Rome, on one side, and hotels in Milan and Rome, on the other, than between Airbnb and hotels in the same city. Research limitations/implications The findings show a progressive de-synchronization (within mutual information) among the five groups of Airbnb hosts triggered by the rising professionalization degree. This result suggests the existence of a partial different business model for multi-listing hosts. Practical implications The study illustrates an absence of any substitution threat between Airbnb and hotels in both cities. This could have important consequences, especially for the pricing and revenue management policy. In fact, the higher the substitution threat, the higher the attention that Airbnb entrepreneurs should pay to the pricing strategy implemented by hotels, and vice versa. Originality/value This study sheds new light on the competition threat between Airbnb and hotels. In this study, hotels and Airbnb hosts appear as two very separate markets.


2010 ◽  
Author(s):  
Corné Hoogendoorn ◽  
Tristan Whitmarsh ◽  
Nicolas Duchateau ◽  
Federico M. Sukno ◽  
Mathieu De Craene ◽  
...  

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2158
Author(s):  
Xin Zhang ◽  
Jiwei Qin ◽  
Jiong Zheng

For personalized recommender systems, matrix factorization and its variants have become mainstream in collaborative filtering. However, the dot product in matrix factorization does not satisfy the triangle inequality and therefore fails to capture fine-grained information. Metric learning-based models have been shown to be better at capturing fine-grained information than matrix factorization. Nevertheless, most of these models only focus on rating data and social information, which are not sufficient for dealing with the challenges of data sparsity. In this paper, we propose a metric learning-based social recommendation model called SRMC. SRMC exploits users’ co-occurrence patterns to discover their potentially similar or dissimilar users with symmetric relationships and change their relative positions to achieve better recommendations. Experiments on three public datasets show that our model is more effective than the compared models.


2020 ◽  
Vol 62 (2) ◽  
pp. 817-831
Author(s):  
Yanghong Zhang ◽  
Feng ◽  
Sun ◽  
Liwei ◽  
Tian ◽  
...  

Author(s):  
Andrew Adinetz ◽  
Jiri Kraus ◽  
Markus Axer ◽  
Marcel Huysegoms ◽  
Stefan Köhnen ◽  
...  

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