A BERT-Based Multi-Criteria Recommender System for Hotel Promotion Management

Numerous reviews are posted every day on travel information sharing platforms and sites. Hotels want to develop a customer recommender system to quickly and effectively identify potential target customers. TripAdvisor, the travel website that provided the data used in this study, allows customers to rate the hotel based on six criteria: Value, Service, Location, Room, Cleanliness, and Sleep Quality. Existing studies classify reviews into positive, negative, and neutral by extracting sentiment terms through simple sentimental analysis. However, this method has limitations in that it does not consider various aspects of hotels well. Therefore, this study performs fine-tuning the BERT (Bidirectional Encoder Representations from Transformers) model using review data with rating labels on the TripAdvisor site. This study suggests a multi-criteria recommender system to recommend a suitable target customers for the hotel. As the rating values of six criteria of TripAdvisor are insufficient, the proposed recommender system uses fine-tuned BERT to predict six criteria ratings. Based on this predicted ratings, a multi-criteria recommender system recommends personalized Top-N customers for each hotel. The performance of the multi-criteria recommender system suggested in this study is better than that of the benchmark system, a single-criteria recommender system using overall ratings.

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Usability of a Machine-Learning Clinical Order Recommender System Interface for Clinical Decision Support and Physician Workflow

10.1101/2020.02.24.20025890 ◽

2020 ◽

Author(s):

Andre Kumar ◽

Jonathan Chiang ◽

Jason Hom ◽

Lisa Shieh ◽

Rachael Aikens ◽

...

Keyword(s):

Recommender System ◽

Recommendation System ◽

Clinical Decision ◽

Order Entry ◽

Manual Search ◽

System A ◽

The Difference ◽

System Order ◽

Better Than ◽

Electronic Order

AbstractObjectiveTo determine whether clinicians will use machine learned clinical order recommender systems for electronic order entry for simulated inpatient cases, and whether such recommendations impact the clinical appropriateness of the orders being placed.Materials and Methods43 physicians used a clinical order entry interface for five simulated medical cases, with each physician-case randomized whether to have access to a previously-developed clinical order recommendation system. A panel of clinicians determined whether orders placed were clinically appropriate. The primary outcome was the difference in clinical appropriateness scores of orders for cases randomized to the recommender system. Secondary outcomes included usage metrics and physician opinions.ResultsClinical appropriateness scores for orders were comparable for cases randomized to the recommender system (mean difference -0.1 order per score, 95% CI:[-0.4, 0.2]). Physicians using the recommender placed more orders (mean 17.3 vs. 15.7 orders; incidence ratio 1.09, 95% CI:[1.01-1.17]). Case times were comparable with the recommender system. Order suggestions generated from the recommender system were more likely to match physician needs than standard manual search options. Approximately 95% of participants agreed the system would be useful for their workflows.DiscussionMachine-learned clinical order options can meet physician needs better than standard manual search systems. This may increase the number of clinical orders placed per case, while still resulting in similar overall clinically appropriate choices.ConclusionsClinicians can use and accept machine learned clinical order recommendations integrated into an electronic order entry interface. The clinical appropriateness of orders entered was comparable even when supported by automated recommendations.

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Application Fuzzy Logic System for Accurate Sheet Trim Shower Position

ACMIT Proceedings ◽

10.33555/acmit.v3i1.43 ◽

2019 ◽

Vol 3 (1) ◽

pp. 186-192

Author(s):

Yudi Wibawa

Keyword(s):

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Dc Motor ◽

Automation System ◽

Paper Making ◽

System A ◽

Accurate Position ◽

And Performance ◽

Better Than ◽

Logic System

This paper aims to study for accurate sheet trim shower position for paper making process. An accurate position is required in an automation system. A mathematical model of DC motor is used to obtain a transfer function between shaft position and applied voltage. PID controller with Ziegler-Nichols and Hang-tuning rule and Fuzzy logic controller for controlling position accuracy are required. The result reference explains it that the FLC is better than other methods and performance characteristics also improve the control of DC motor.

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Municipal wastewater treatment with pond–constructed wetland system: a case study

Water Science & Technology ◽

10.2166/wst.2005.0491 ◽

2005 ◽

Vol 51 (12) ◽

pp. 325-329 ◽

Cited By ~ 18

Author(s):

X. Wang ◽

X. Bai ◽

J. Qiu ◽

B. Wang

Keyword(s):

Constructed Wetland ◽

Municipal Wastewater ◽

Municipal Wastewater Treatment ◽

Oxidation Pond ◽

System A ◽

Treated Effluent ◽

Wetland System ◽

Oxidation Ponds ◽

Better Than

The performance of a pond–constructed wetland system in the treatment of municipal wastewater in Kiaochow city was studied; and comparison with oxidation ponds system was conducted. In the post-constructed wetland, the removal of COD, TN and TP is 24%, 58.5% and 24.8% respectively. The treated effluent from the constructed wetland can meet the Chinese National Agricultural and Irrigation Standard. The comparison between pond–constructed wetland system and oxidation pond system shows that total nitrogen removal in a constructed wetland is better than that in an oxidation pond and the TP removal is inferior. A possible reason is the low dissolved oxygen concentration in the wetland. Constructed wetlands can restrain the growth of algae effectively, and can produce obvious ecological and economical benefits.

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News recommender system: a review of recent progress, challenges, and opportunities

Artificial Intelligence Review ◽

10.1007/s10462-021-10043-x ◽

2021 ◽

Author(s):

Shaina Raza ◽

Chen Ding

Keyword(s):

Recommender System ◽

Recent Progress ◽

System A ◽

Challenges And Opportunities

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Agricultural Loan Recommender System - A Machine Learning Approach

2021 International Conference on Innovative Trends in Information Technology (ICITIIT) ◽

10.1109/icitiit51526.2021.9399592 ◽

2021 ◽

Author(s):

Arsal Imtiaz ◽

S Nachiket ◽

K.V Nishanth ◽

Jyoti Angadi ◽

T.C Pramod

Keyword(s):

Machine Learning ◽

Recommender System ◽

Learning Approach ◽

System A ◽

Machine Learning Approach

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An Overview of the Recommender System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.302.787 ◽

2013 ◽

Vol 302 ◽

pp. 787-791

Author(s):

Lu Zhao ◽

Rong Rong Yang ◽

Meng Zhai ◽

Feng Ming Liu

Keyword(s):

Recommender System ◽

System A ◽

The Future

Delivering recommendation services are the trend of the future, so Recommender System varied very vital and widely applied in e-commerce websites to help customers in finding the items they want. A recommender system should be able to provide users with useful information about the items that might be interesting to them. The ability of immediately responding to changes in users preferences is a valuable asset for such systems. In recommender system, a variety of methods have been emerged as the basis for recommender. However, existing recommendation methods have the limitation. To overcome this limitation, we will propose new recommender system by combining the existing techniques. So, we firstly give an overview of recommender system for the future researches.

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Application of Improved Recommendation System Based on Spark Platform in Big Data Analysis

Cybernetics and Information Technologies ◽

10.1515/cait-2016-0092 ◽

2016 ◽

Vol 16 (6) ◽

pp. 245-255 ◽

Cited By ~ 1

Author(s):

Li Xie ◽

Wenbo Zhou ◽

Yaosen Li

Keyword(s):

Big Data ◽

Recommender System ◽

Recommendation System ◽

Parallel Implementation ◽

Implementation Process ◽

Recommendation Algorithm ◽

Filtration Problem ◽

Information Filtration ◽

Similarity Information ◽

Better Than

Abstract In the era of big data, people have to face information filtration problem. For those cases when users do not or cannot express their demands clearly, recommender system can analyse user’s information more proactive and intelligent to filter out something users want. This property makes recommender system play a very important role in the field of e-commerce, social network and so on. The collaborative filtering recommendation algorithm based on Alternating Least Squares (ALS) is one of common algorithms using matrix factorization technique of recommendation system. In this paper, we design the parallel implementation process of the recommendation algorithm based on Spark platform and the related technology research of recommendation systems. Because of the shortcomings of the recommendation algorithm based on ALS model, a new loss function is designed. Before the model is trained, the similarity information of users and items is fused. The experimental results show that the performance of the proposed algorithm is better than that of algorithm based on ALS.

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A Life-long Learning Recommender System to Promote Employability

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v12i06.7166 ◽

2017 ◽

Vol 12 (06) ◽

pp. 77 ◽

Cited By ~ 3

Author(s):

David Baneres ◽

Jordi Conesa

Keyword(s):

Recommender System ◽

Unemployed People ◽

Knowledge And Skills ◽

Professional Career ◽

System A ◽

Life Long Learning ◽

University Courses ◽

Job Offers ◽

New Challenges

Is my professional knowledge outdated? Do I have the skills needed for the new challenges of the society? What knowledge do I lack to qualify for a job I like? What universities can I address to get knowledge that improves my employment expectations? These are relevant questions that all employees have done in any moment of their life. In addition, when there are high rates of unemployment and job offers that keep unfilled, the answers to these questions are even more relevant. Answering such questions open new opportunities for employed and unemployed people, by allowing them to design a formative plan according to their skills and expectations. It also provides evidences to employers about the skills and knowledge of the society, making them more aware of the skills of their potential future employees. The companies also will have more knowledge to design the professional career of their employees according to the company needs and the knowledge and skills of their employees. This paper proposes a system that helps people by showing which knowledge and skills a person misses for a given job position and what university courses the person can take to acquire the required skills and knowledge. The system has been implemented as a recommender system that helps users in planning their life-long learning. The paper shows the architecture of the proposed system, a case study to explain how it works, a survey to validate its usefulness and usability and some conclusions after its first experimentation.

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