Journal Article10.4018/978-1-6684-4730-7.ch009
Recommendation System
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TL;DR: In this paper , RapidMiner can analyze data following the principles of the recommendation system, as demonstrated in this chapter, step-by-step, using probabilistic methods to analyze data and provide product suggestions.
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Abstract: Recommendation systems are critical tools used by marketing departments to provide customers with product recommendations. Data scientists also use recommendation system analysis to assess the effectiveness of product and service suggestions. There are two types of recommendation systems: content-based and collaborative filtering. Content-based recommendations are based on the customer's purchase history, while collaborative filtering suggests a product based on purchasing behavior. Collaborative filtering can be divided into content-based filtering, which suggests products based on similar purchasing behavior, and item-based filtering, which suggests products based on their attributes. User-based and item-based nearest-neighbor collaborative filtering and probabilistic methods are used to analyze data and provide product suggestions. Businesses rely on recommendation systems to achieve various objectives such as customer retention and increased ROI. RapidMiner can analyze data following the principles of the recommendation system, as demonstrated in this chapter, step-by-step.
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Citations
Recommendation System for Retail Business Using Customer Segmentation: Case Study of Tuenjai Company in Surat Thani, Thailand
Pattarawan Gunglin,Siriwan Kajornkasirat,Kritsada Puangsuwan +2 more
- 28 Feb 2024
TL;DR: This research aimed to develop a recommendation system for the retail store using customer segmentation based on customer purchase behavior by RFM (Recency, Frequency, and Monetary) analysis and k-means clustering and used the FP-Growth algorithm to find the association rules.
References
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D.A. Adeniyi,Z. Wei,Y. Yongquan +2 more
TL;DR: The result shows that the K-Nearest Neighbor classifier is transparent, consistent, straightforward, simple to understand, high tendency to possess desirable qualities and easy to implement than most other machine learning techniques specifically when there is little or no prior knowledge about data distribution.
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Career Recommendation Systems using Content based Filtering
Tanya V Yadalam,Vaishnavi M Gowda,Vanditha Shiva Kumar,Disha Girish,Namratha M +4 more
- 10 Jun 2020
TL;DR: Examination of existing career recommendation system is examined and the drawbacks of these systems are highlighted, such as cold start, scalability and sparsely.
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Content-Based Recommendation Using Machine Learning
Yifan Tai,Zhenyu Sun,Zixuan Yao +2 more
- 25 Oct 2021
TL;DR: In this article, a three-step profiling method is adopted in order to better capture users' profiles, a purchase item prediction is made based on Logistic Regression, a category prediction was made by support vector machine (SVM), and a user's rating prediction by convolutional neural network (CNN) and Long Short-Term Memory (LSTM).
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Item-Based Collaborative Filtering and Association Rules for a Baseline Recommender in E-Commerce
Jessica Lourenco,Aparna S. Varde +1 more
- 10 Dec 2020
TL;DR: In this article, item-based collaborative filtering and association rule mining are explored over Amazon review data on cellphones and accessories, and a baseline recommender system scalable to larger data is built.
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Designing a tourism recommendation system using a hybrid method (Collaborative Filtering and Content-Based Filtering)
Ni Wayan Priscila Yuni Praditya,Adhistya Erna Permanasari,Indriana Hidayah +2 more
- 17 Jul 2021
TL;DR: In this article, the authors used the Hybrid (Collaborative Filtering and Content-Based Filtering) method and the Nearest Neighbor (NN) algorithm to produce recommendation items under the user's wishes.
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