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Solution_​Round_​4_​Team_​AST (Public_​Version)

<p>This workflow is Team AST’s solution for the Round of 4 challenge in the KNIME Game of Nodes 2024. Some modifications have been made to the originally submitted workflow.<br>------------------------------------------------------------------------------------------------------------------------------------------------------------------------------<br><br><br>Product Recommendation Engine<br><br>Challenge description:<br><br>You work as a data scientist at Amazon, one of the world's largest online retailers. To drive more sales and deliver valuable suggestions to customers, your manager has tasked you with building a product recommendation engine. Based on the product ratings given by customers, recommend 5 other products for each product in the given set of products. You are free to choose the approach that you see fits best. Clearly, in the interest of the company's sales, your recommendations should be meaningful to customers.<br><br>Additionally, your manager requires you to present the obtained recommendations in an interactive dashboard, and provide an explanation of your approach in an annotation (max. 200 words).<br><br>Note: Considering the dataset size, the recommendation engine can be computationally expensive to run. Feel free to use a small sample of the dataset, if computational resources are limited.<br><br>Key requirement: you must provide an explanation of your approach where you also clarify why you think your recommendation engine is good and would benefit the company and the customers.<br><br>Outcome:&nbsp;<br><br>An item-item recommender system and an interactive dashboard to present the recommendations.<br><br>Deliver your solution as a separate workflow and name it: Solution_Round_4_. Place your solution workflow in the same folder of this challenge workflow.<br><br>Teams are strongly encouraged to submit high-quality work in order to improve their chances of getting maximum points. Don't be afraid to go the extra mile! :)<br><br>Dataset:<br><br>Provided in challenge folder.<br><br>Deadline:<br><br>April 7, 2024 (submission by 11:59 PM CET) **. Check the calendar of the tournament: https://info.knime.com/game-of-nodes<br><br>** We will verify the date and time of the latest edits.<br><br>KNIME Game of Nodes:<br><br>Rules, Assessment Criteria &amp; FAQs: https://info.knime.com/game-of-nodes</p>

URL: KNIME Game of Nodes https://info.knime.com/game-of-nodes

Solution_Round_4_Team_AST
Our system is a product recommendation system that combines multiple approaches from the following four perspectives: 1. Items frequently bought together : Frequently purchased together and high rated products enhances their value by combination use. 2. #1 Best Seller in the Topic: These items are offered as highly reliable options. 3. Items Selected via Association Rules or Collaborative Filtering (CF): Personalized recommendations based on users’ purchase history and ratings increase satisfaction and loyalty. CF is implemented when a user ID is provided. 4. Highly Rated and Frequently Purchased Items in the Same Topic: A powerful indicator, increasing the likelihood of purchases by new customers. The priority of recommendations is in numerical order. By combining multiple approaches, we can cover a wide range of customers, including new ones. For existing customers, better product recommendations can be made by utilizing their purchase and rating history. This recommendation system enhances the shopping experience, boosts purchase intent, and fosters brand loyalty by facilitating efficient exploration and comparison, especially in time-constrained online shopping.
Product Recommendation EngineChallenge description:You work as a data scientist at Amazon, one of the world's largest online retailers. To drive more sales and deliver valuable suggestions to customers, your manager has tasked you with building a product recommendation engine. Based on the product ratings given by customers, recommend 5 other products for each product in the given set of products. You are free to choose the approach that you see fits best. Clearly, in the interest of the company's sales, your recommendations should be meaningful to customers.Additionally, your manager requires you to present the obtained recommendations in an interactive dashboard, and provide an explanation of your approach in an annotation (max. 200 words).Note: Considering the dataset size, the recommendation engine can be computationally expensive to run. Feel free to use a small sample of the dataset, if computational resources are limited.Key requirement: you must provide an explanation of your approach where you also clarify why you think your recommendation engine is good and would benefit the company and the customers.Outcome:&nbsp;An item-item recommender system and an interactive dashboard to present the recommendations.Deliver your solution as a separate workflow and name it: Solution_Round_4_&lt;your_team_name&gt;. Place your solution workflow in the same folder of this challenge workflow.Teams are strongly encouraged to submit high-quality work in order to improve their chances of getting maximum points. Don't be afraid to go the extra mile! :)Dataset:Provided in challenge folder.Deadline:April 7, 2024 (submission by 11:59 PM CET) **. Check the calendar of the tournament: https://info.knime.com/game-of-nodes** We will verify the date and time of the latest edits.KNIME Game of Nodes:Rules, Assessment Criteria &amp; FAQs: https://info.knime.com/game-of-nodes
How to use 1. execute Recommendation System and open view 2. select whether you have a User ID. If you have a User ID, enter it. (You can do this in the default sample) 3. select one item and press the Buy button.
Recommendation System
Component
Read Product_history.csv
CSV Reader
For each product in this given set of products,what other 5 products will you recommend?
Table Creator
Data processing
Metanode

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Links