Estimating translation probabilities for social tag suggestion

Chen, Xinxiong; Liu, Zhiyuan; Sun, Maosong

doi:10.1016/j.eswa.2014.10.002

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…122,[183][184][185][186][187][188][189][190][191][192][193][194] 2. Others: In recommendations systems, authors also highlight some methods with the tagged system, such as real-time location, taxi finding, and user identity 3,13,35,52,54,88,108,110,129,184,185,188,189,[195][196][197][198][199][200][201][202][203]…”

Section: 310mentioning

confidence: 99%

“…2. Probabilistic: Some of the methods adopted in this category are the root of the mean square error (RMSE), mean absolute error (MAE) and so forth applied to the recommender system 23,28,34,46,51,53,87,109,149,195,[235][236][237][238][239] 3. Ranking: To categorize and evaluate the items or users based on ranking, give the idea of how much the first user is nobler than the second.…”

Section: Recommender Systems Evaluation Techniquesmentioning

confidence: 99%

“…Most authors highlighted a social tagging system for better recommendations 122,183‐194 Others : In recommendations systems, authors also highlight some methods with the tagged system, such as real‐time location, taxi finding, and user identity 3,13,35,52,54,88,108,110,129,184,185,188,189,195‐203 …”

Section: Investigation and Analysis Questions With Classificationmentioning

confidence: 99%

“…We showed the evaluation techniques in the following groups: Qualitative : Major role of this evaluation technique is to lower the error. Some approaches used are accuracy, Inference Accuracy adopted by authors 14,15,23,30,57,87,114,147,148,155,171,232‐234 in taxi recommendation. Probabilistic : Some of the methods adopted in this category are the root of the mean square error (RMSE), mean absolute error (MAE) and so forth applied to the recommender system 23,28,34,46,51,53,87,109,149,195,235‐239 Ranking : To categorize and evaluate the items or users based on ranking, give the idea of how much the first user is nobler than the second. Some examples are normalized discount cumulative gain (NDCG), precision, recall, F1 and so forth 42,46,52,55,192,240‐244 …”

Section: Investigation and Analysis Questions With Classificationmentioning

confidence: 99%

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Towards the significance of taxi recommender systems in smart cities

Katarya

2022

Concurrency and Computation

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Since their launch in the early 1990's, recommender systems (RSs) have played an essential role in information filtering and providing personalized information to users by utilizing the web, mobile, context, personalized, and practical applications. Many research articles have been published in various application domains such as movies, books, documents, news, images, music, shopping, TV programs, tourism, group, social, taxi, and others. However, from these domains, taxi recommendation is a new and thrust area of research, as publications in this domain are few and recently published.Taxi recommendations are associated with drivers-passengers with their: behavior, hotspots, trajectory, point of interest, route planning or prediction, taxi finding, anomaly or fraud detection, urban computing, mobility patterns and so forth. Due to this diversity and various agendas in taxi recommendations, more exploration is required, as it is a less mature and growing field than other domains. So we have done a literature review in the taxi domain by classification, incorporating reputed articles published from 2001 to August 2022. We include the reputed papers which highlight, analyze and perform studies only on the taxi domain. This article categorizes and discusses the research and development in taxi recommendations as nature, approaches, algorithms, methods, datasets, models, devices, information, and evaluation techniques. This survey article will actively help the researchers and professionals understand the current situations in taxi RS and resolve future trends, opportunities, and research.

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