在這個資訊爆炸的時代，人們生活中許多大大小小的事，例如:晚餐要吃什麼？出門要穿什麼？如何規劃旅遊行程？要選擇在哪個地方成家立業？都會面臨著無數的選擇。對於消費者而言，在如此大量的選擇中，如何做出對自己最適合的選擇，成為了一個艱辛的過程，人們時常花費大量的時間在網路上尋找各個選項的優點與缺點，但最後還是不能很確定到底哪個適合自己。對於商家來說，如何讓自己的產品在大量的訊息中脫穎而出獲得消費者的青睞，成為了業務增長的關鍵。於是有了許多推薦系統的出現。
在推薦系統中，許多的推薦系統是透過網路上的評論為依據推薦產品給消費者，而在網路上的評論中，夾雜著各式各樣的非理性評論，像是商業目的性的評論，商家推出留下好評就有優惠的活動、廠商花錢請人撰寫的業配評論、甚至有購買評論攻擊類似產品或是讓自己的產品分數上升的行為出現。大多數的網站只處理有問題或是爭議的評論，亞馬遜、Yelp與google map只會刪除可能虛假的評論，對於商業目的的評論沒有方法可以約束，這些評論往往會欺騙到消費者，甚至大大影響推薦系統推薦給消費者的產品，因此如何獲得並整理有效的評論是非常重要的。
本論文設計與實作了一個容器化評論管理與推薦系統，系統的目標為收集使用者的真實評論與提供使用者適合的推薦清單，使用者必須登入系統才能撰寫評論，且無法觀看其他人的評論，可以避免商家推出商業性活動，因為沒有辦法達到宣傳的效果。使用者可以透過系統來獲得推薦清單，省去了需要自己判斷他人評論的時間，推薦清單會根據使用者的歷史評論來推薦，因此使用者如果希望獲得更符合自己需求的推薦，就會更願意留下自己真實的評論。系統包含負載平衡、任務排程、API權杖與資料隱私設計，透過容器化，讓系統擁有可攜性、可擴展性、容錯能力與敏捷性，系統可以非常容易更換推薦演算法，並且根據放入資料庫的不同，能夠切換為不同的評論管理與推薦系統，例如:放入餐廳相關的資料，網頁系統會自動建構為餐廳評論管理與推薦系統。放入電子產品相關資料，網頁系統會自動建構為電子產品評論管理與推薦系統。只需要更換推薦演算法與資料庫的容器資料，網頁程式部分完全不需要做任何修改。本系統可以透過系統的API讓外部使用者來獲取評論資料，進行數據分析及應用。

In this era of information explosion, people must make countless choices, such as deciding what to eat for dinner. What to wear when going out? How to plan a travel itinerary? Where to choose to start a family? As far as consumers are concerned, they often spend much time looking for the advantages and disadvantages of various options on the Internet, but in the end, they are still trying to decide which one is right for them. For merchants, how to make their products stand out in many messages and win the favor of consumers has become the key to business growth. As a result, lots of recommendation systems have emerged.
In recommendation systems, many recommend products to consumers based on online reviews. These reviews are often mixed with irrational thoughts, such as commercial-purpose ones. Merchants launch promotional activities with favorable reviews, or manufacturers pay people to write professional matching reviews and even purchase reviews to attack similar products or increase their product scores. Most websites only deal with problematic or controversial reviews. There is no way to restrict reviews for commercial purposes. These reviews often deceive consumers and even significantly affect the products recommended by the recommendation system consumers. Therefore, how to obtain and organize effective reviews is very important.
This paper designs and implements a containerized reviews management and recommendation system. The system's goal is to collect honest reviews from users and provide a list of recommendations suitable for users. Users must be logged in to write a review and cannot view other people's reviews, which can prevent merchants from launching commercial activities because there is no way to achieve the effect of publicity. However, users can obtain the recommendation list through the system, saving time in judging other people's reviews. The recommendation list will be recommended based on the user's historical reviews, so if users want to get recommendations that better meet their needs, they will be more willing to leave their own real reviews. The system includes load balancing, task scheduling, API scepter, and data privacy design. The system has portability, scalability, fault tolerance, and agility through containerization. The system can easily replace the recommendation algorithm, and according to the release depending on the input database, it can be switched to a different review management and recommendation system. For example, the webpage system will automatically build a restaurant review management and recommendation system if you put in restaurant-related information. Put in relevant information about electronic products, and the webpage system will automatically construct a review management and recommendation system for electronic products. Only the recommendation algorithm and the container data of the database need to be replaced, and the webpage program part does not need to be modified. This system can allow external users to obtain review data through the system's API for data analysis and application.

[1] A. E. Tanudjaja, V. H. Siady, V. Meidianto, D. W. Sukmaningsih, E. Halim and Ferdianto, "The Impact of Online Review on Customers Patronage Intention on Restaurant or Eating Places," 2022 International Conference on Information Management and Technology （ICIMTech）, 2022, pp. 511-516, doi： 10.1109/ICIMTech55957.2022.9915270.
[2] L. T. T. Tran, ‘Online reviews and purchase intention： A cosmopolitanism perspective’, Tourism Management Perspectives, vol. 35, p. 100722, 2020.
[3] G. Tian, L. Lu, and C. McIntosh, ‘What factors affect consumers’ dining sentiments and their ratings： Evidence from restaurant online review data’, Food Quality and Preference, vol. 88, p. 104060, 2021.
[4] L. Arora and B. K. S. Mail, ‘Influence of review quality, review quantity and review credibility on purchase intention in context of high involvement products’, European Journal of Applied Business and Management, vol. 4, no. 4, 2018.
[5] M. Zhou, M. Liu, and D. Tang, ‘Do the characteristics of online consumer reviews bias buyers’ purchase intention and product perception？ A perspective of review quantity, review quality and negative review sequence’, International Journal of Services Technology and Management 11, vol. 19, no. 4–6, pp. 166–186, 2013.
[6] D. Mayzlin, Y. Dover, and J. Chevalier, ‘Promotional Reviews: An Empirical Investigation of Online Review Manipulation’, American Economic Review, vol. 104, no. 8, pp. 2421–2455, Aug. 2014.
[7] K. R. Shrote and A. V. Deorankar, "Review based service recommendation for big data," 2016 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, India, 2016, pp. 470-474, doi: 10.1109/AEEICB.2016.7538334.
[8] C. Chen, "Research on personalized recommendation algorithm based on fusion product review evaluation index," 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), Fuzhou, China, 2021, pp. 209-212, doi: 10.1109/CEI52496.2021.9574587.
[9] P. K. Jain, G. Srivastava, J. C. -W. Lin and R. Pamula, "Unscrambling Customer Recommendations: A Novel LSTM Ensemble Approach in Airline Recommendation Prediction Using Online Reviews," in IEEE Transactions on Computational Social Systems, vol. 9, no. 6, pp. 1777-1784, Dec. 2022, doi: 10.1109/TCSS.2022.3200890.
[10] H. Liu, H. Qiao, X. Shi and M. Shang, "Aspect-aware Asymmetric Representation Learning Network for Review-based Recommendation," 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022, pp. 1-8, doi: 10.1109/IJCNN55064.2022.9892559.
[11] R. K. Chaurasiya and U. Sahu, "Improving Performance of Product Recommendations Using User Reviews," 2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering （ICRAIE）, 2018, pp. 1-4, doi： 10.1109/ICRAIE.2018.8710414.
[12] R. S. de O. Júnior, R. C. A. da Silva, M. S. Santos, D. W. Albuquerque, H. O. Almeida and D. F. S. Santos, "An Extensible and Secure Architecture based on Microservices," 2022 IEEE International Conference on Consumer Electronics （ICCE）, 2022, pp. 01-02, doi： 10.1109/ICCE53296.2022.9730757.
[13] B. Bilgin, H. Unlu and O. Demirörs, "Analysis and Design of Microservices： Results from Turkey," 2020 Turkish National Software Engineering Symposium （UYMS）, 2020, pp. 1-6, doi： 10.1109/UYMS50627.2020.9247022.
[14] O. Al-Debagy and P. Martinek, "A Comparative Review of Microservices and Monolithic Architectures," 2018 IEEE 18th International Symposium on Computational Intelligence and Informatics （CINTI）, 2018, pp. 000149-000154, doi： 10.1109/CINTI.2018.8928192.
[15] A. M, A. Dinkar, S. C. Mouli, S. B and A. A. Deshpande, "Comparison of Containerization and Virtualization in Cloud Architectures," 2021 IEEE International Conference on Electronics, Computing and Communication Technologies （CONECCT）, 2021, pp. 1-5, doi： 10.1109/CONECCT52877.2021.9622668.
[16] D. Reis, B. Piedade, F. F. Correia, J. P. Dias and A. Aguiar, "Developing Docker and Docker-Compose Specifications: A Developers’ Survey," in IEEE Access, vol. 10, pp. 2318-2329, 2022, doi: 10.1109/ACCESS.2021.3137671.
[17] G. R. Voth, C. Kindel and J. Fujioka, "Distributed application development for three-tier architectures: Microsoft on Windows DNA," in IEEE Internet Computing, vol. 2, no. 2, pp. 41-45, March-April 1998, doi: 10.1109/4236.670682.
[18] M. Haekal and Eliyani, "Token-based authentication using JSON Web Token on SIKASIR RESTful Web Service," 2016 International Conference on Informatics and Computing (ICIC), Mataram, Indonesia, 2016, pp. 175-179, doi: 10.1109/IAC.2016.7905711.
[19] E. Qin, Y. Wang, L. Yuan and Y. Zhong, "Research on Nginx Dynamic Load Balancing Algorithm," 2020 12th International Conference on Measuring Technology and Mechatronics Automation （ICMTMA）, 2020, pp. 620-624, doi： 10.1109/ICMTMA50254.2020.00138.
[20] Z. Wen, G. Li and G. Yang, "Research and Realization of Nginx-based Dynamic Feedback Load Balancing Algorithm," 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2018, pp. 2541-2546, doi: 10.1109/IAEAC.2018.8577911.
[21] W. M. C. J. T. Kithulwatta, K. P. N. Jayasena, B. T. G. S. Kumara and R. M. K. T. Rathnayaka, "Performance Evaluation of Docker-based Apache and Nginx Web Server," 2022 3rd International Conference for Emerging Technology (INCET), 2022, pp. 1-6, doi: 10.1109/INCET54531.2022.9824303.
[22] Aiqun Zhu, "Research on web protocol in the implementation of e-commerce web site security," 2011 International Conference on Computer Science and Service System (CSSS), 2011, pp. 1673-1675, doi: 10.1109/CSSS.2011.5974957.
[23] M. Data, M. Luthfi and W. Yahya, "Optimizing single low-end LAMP server using NGINX reverse proxy caching," 2017 International Conference on Sustainable Information Engineering and Technology （SIET）, 2017, pp. 21-23, doi： 10.1109/SIET.2017.8304102.
[24] I. A. Bairagi, A. Sharma, B. K. Rana and A. Singh, "UNO： A Web Application using Django," 2021 3rd International Conference on Advances in Computing, Communication Control and Networking （ICAC3N）, 2021, pp. 1371-1374, doi： 10.1109/ICAC3N53548.2021.9725577.
[25] M. A. Tariq, K. Gupta and T. Poongodi, "Cloud-Based E-Learning Platform using Django," 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), 2022, pp. 809-814, doi: 10.1109/ICIEM54221.2022.9853046.
[26] P. Seda, J. Hosek, P. Masek and J. Pokorny, "Performance testing of NoSQL and RDBMS for storing big data in e-applications," 2018 3rd International Conference on Intelligent Green Building and Smart Grid （IGBSG）, 2018, pp. 1-4, doi： 10.1109/IGBSG.2018.8393559.
[27] S. S. Prakash and B. C. Kovoor, "Performance optimisation of web applications using In-memory caching and asynchronous job queues," 2016 International Conference on Inventive Computation Technologies （ICICT）, 2016, pp. 1-5, doi： 10.1109/INVENTIVE.2016.7830234.
[28] P. K. Gupta and N. Rakesh, "Different Job Scheduling Methodologies for Web Application and Web Server in a Cloud Computing Environment," 2010 3rd International Conference on Emerging Trends in Engineering and Technology, 2010, pp. 569-572, doi： 10.1109/ICETET.2010.24.
[29] A. Pathak and C. Kalaiarasan, "RabbitMQ Queuing Mechanism of Publish Subscribe model for better Throughput and Response," 2021 Fourth International Conference on Electrical, Computer and Communication Technologies （ICECCT）, 2021, pp. 1-7, doi： 10.1109/ICECCT52121.2021.9616722.
[30] X. J. Hong, H. Sik Yang and Y. H. Kim, "Performance Analysis of RESTful API and RabbitMQ for Microservice Web Application," 2018 International Conference on Information and Communication Technology Convergence （ICTC）, 2018, pp. 257-259, doi： 10.1109/ICTC.2018.8539409.