Recommendations of closed consensus temporal patterns by group decision making

Huang, Tony Cheng-Kui

doi:10.1016/j.knosys.2013.10.003

Cited by 6 publications

(5 citation statements)

References 46 publications

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“…F-measure, which is defined as the harmonic mean of the recall and precision, is broadly utilized to assess the quality of RSs. e recall and precision measures are provided in equation (21) and equation (22). In equation ( 23), we present F-measure [74].…”

Section: Methods Evaluation and Comparisonsmentioning

confidence: 99%

“…Recommender Systems (RSs) based on fuzzy logic have been advanced since 2008 [21]. Hence, several researchers have explored fuzzy logic in several domains related to recommender systems like consensus ranking [22], item and trust-aware collaborative filtering [23], correlation based similarity [24], competence RSs [25], situation-aware collaborative filtering (CF) [26], tourism system [27], stock market [28], movie RSs [29], automatic group RSs [30], knowledge-based RSs [31], and multicriteria collaborative filtering [32].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach

Nilashi

Abumalloh

Alrizq

et al. 2022

Mathematical Problems in Engineering

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Travel recommendation agents have been a helpful tool for travelers in their decision-making for destination choices. It has been shown that sparsity can significantly impact on the accuracy of recommendation agents. The COVID-19 outbreak has affected the tourism and hospitality industry of almost all countries in the world. Tourists who have planned to travel are canceling or postponing trips due to this pandemic. Accordingly, this will impact the rate of travelers’ online reviews on tourism products. Hence, the lack of data, in terms of ratings and textual reviews on hotels, will be a major issue for travel recommendation agents during the COVID-19 outbreak in the context of tourism and hospitality. This will be a new challenge for the researchers in the development of travel recommendation agents. Machine learning has been found to be effective in dealing with the data sparsity in recommendation agents. Therefore, developing new algorithms would be helpful to overcome the sparsity issue in travel recommendation agents. This research provides a new method through neurofuzzy, dimensionality reduction, and clustering techniques and evaluates it on the TripAdvisor dataset to see its effectiveness in solving the sparsity issue. The results showed that the method which used the fuzzy logic technique with the aid of clustering, dimensionality reduction, and fuzzy logic is more efficient in addressing the sparsity problem and presenting more accurate results. The results of the method evaluation are presented and discussed, and several suggestions are provided for future studies.

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Section: Methods Evaluation and Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach

Nilashi

Abumalloh

Alrizq

et al. 2022

Mathematical Problems in Engineering

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show abstract

“…Some examples are normalized discount cumulative gain (NDCG), precision, recall, F1 and so forth. 42,46,52,55,192,[240][241][242][243][244] 4. Passengers and taxi drivers: These are authentic for trust, explanation and group behavior.…”

Section: Recommender Systems Evaluation Techniquesmentioning

confidence: 99%

“…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 Passengers and taxi drivers : These are authentic for trust, explanation and group behavior 24,26,28,41,115 …”

Section: Investigation and Analysis Questions With Classificationmentioning

confidence: 99%

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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“…[92], [195], [96], [196], [97], [197], [93], [198], [98], [99] , [78], [82], [199]- [201], [91], [202], [203], [200], [90] Case based [204], [205], [206], [207], [136], [138], [189], [127], [139], [208], [209], [142], [145], [143], [128] [65] have presented comprehensive methodology to overcome the issues which is associated with keywords based approaches. Cho et al [21] proposed a personalized recommendation system which is based on Web usage mining.…”

Section: Model Based Techniquesmentioning

confidence: 99%

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

Sohail

Siddiqui

Ali

2017

JESTR

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This paper presents the state of art techniques in recommender systems (RS). The various techniques are diagrammatically illustrated which on one hand helps a naïve researcher in this field to accommodate the on-going researches and establish a strong base, on the other hand it focuses on different categories of the recommender systems with deep technical discussions. The review studies on RS are highlighted which helps in understanding the previous review works and their directions. 8 different main categories of recommender techniques and 19 sub categories have been identified and stated. Further, soft computing approach for recommendation is emphasized which have not been well studied earlier. The major problems of the existing area is reviewed and presented from different perspectives. However, solutions to these issues are rarely discussed in the previous works, in this study future direction for possible solutions are also addressed.

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Recommendations of closed consensus temporal patterns by group decision making

Cited by 6 publications

References 46 publications

A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach

A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach

Towards the significance of taxi recommender systems in smart cities

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

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