Amazon App gives me inspiration that it recommends all the items that customer searches for. so from that app I just thought to implement the idea of Product recommendation system.

What it does

The aim of Product Recommendation System is to recommend product to the users based on their and other consumer’s search and search history using graph database.

How I built it

I built it using technologies like Neo4j and Application type : Spring Boot Java Web application. We implemented the recommendation feature using Neo4j Graph database where we can go to any depth through graph algorithms such as DFS or BFS in linear time complexity.

Challenges I ran into

Many Challenges I ran in like firstly there was an issue of request- response within the system so i resolved it by adding JSON Format.

Accomplishments that I'm proud of

I'm proud of implemented our system using a graph database that processed data quickly as well as recommended products to the customer efficiently in comparatively less time.

What I learned

I learned many technologies that help me to develop our system in an easy way.

What's next for Product Recommendation System

I will try other new technologies like python to implement our system.

Built With

  • 4.1.0
  • 5.0.5
  • access
  • algorithms
  • any
  • application-type-:-spring-boot-java-web-application-web-framework-:-spring-boot-enabled-spring-webmvc
  • as
  • bfs
  • can
  • database
  • depth
  • dfs
  • feature
  • go
  • graph
  • implemented
  • in
  • linear
  • neo4j
  • neo4j-server
  • or
  • persistence
  • recommendation
  • spring-data-neo4j
  • spring-data-rest
  • such
  • the
  • through
  • time
  • to
  • using
  • we
  • where
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