Recommendation system to enhance user experience.

Overview: I developed a recommendation system designed to enhance the eCommerce customer experience. The system was built using a small dataset of book ratings and focused on providing tailored recommendations to both new and existing users. The project emphasized the back-end development of the recommendation engine, utilizing collaborative filtering techniques to deliver personalized suggestions.

Key Contributions:

1. User Identification and Sign-Ups:

   • New vs. Existing Users: Implemented functionality to differentiate between new and existing users.
   • Promotion of Sign-Ups: Designed mechanisms to encourage new users to sign up, enhancing user engagement and data collection.

2. Recommendation Strategies:

   • For New Users: Recommended popular items to new users based on general popularity metrics, ensuring immediate value and engagement.
   • For Existing Users: Implemented both item-based and user-based collaborative filtering techniques to provide personalized recommendations.

3. Collaborative Filtering Techniques:

   • Item-Based Filtering: Utilized the Item Similarity Index to recommend items similar to those previously liked or purchased by users.
   • User-Based Filtering: Employed the User Similarity Index to recommend items based on the preferences of similar users.

4. Data Analysis and Recommendation Logic:

   • Past Purchases and Likes: Analyzed users’ past purchases and likes to generate accurate and relevant recommendations.
   • Similarity Indices: Calculated item and user similarity indices to enhance the precision of the recommendation system.

Impact: This recommendation system significantly improved the eCommerce customer experience by providing tailored product suggestions, leading to increased user satisfaction and engagement. By focusing on collaborative filtering techniques, the system effectively leveraged user data to deliver relevant recommendations, fostering a more personalized shopping experience.

Including this project in your portfolio highlights your expertise in developing advanced recommendation engines and your ability to apply collaborative filtering techniques to real-world datasets. It demonstrates your proficiency in data analysis, machine learning, and back-end development, showcasing your capability to create impactful solutions that enhance user experiences in the eCommerce domain.