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Friday, April 02, 2021

Product Recommendation using a Knowledge Graph

 An example of an interaction between a knowledge graph and a specific task. Have used Neo4j Graphs, so this was instructive. A number of reco examples are shown at the link, beyond this intro.

Amazon Product Recommendation using Neo4j Knowledge Graph

Leveraging knowledge graph capabilities in building a product recommendation system

By Shyam Pratap Singh  in   TowardsdataScience

As we see all around, our choices are driven by recommendations. Our decision about online shopping or going out for dinner or looking for college or watching movies on Netflix, we often need recommendations from our close circles and, based on that, we make the best decisions possible.

Building a product recommendation system is not something new, but leveraging a knowledge graph to achieve this is exciting and amazing. We are going to use Neo4j Graph Database to analyze 100,000 Amazon product records.

This is the satellite view of our galaxy, just kidding :P. Our Amazon product dataset in the graph

So, before talking about graphs, let’s understand a bit about a recommendation system.

Recommendation System:

A recommender system seeks to predict the “rating” or “preference” a user would give to an item. It helps the user to make the best choices.

Recommender systems are used in a variety of areas, with commonly recognized examples taking the form of playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms and open web content recommenders. These systems can operate using a single input, like music, or multiple inputs within and across platforms like news, books, and search queries.

Recommender systems are a useful alternative to search algorithms since they help users discover items they might not have found otherwise..... '

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