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Spring Data: Bridging Multiple Databases

Solution: Book Recommendation System

Explore how to create a book recommendation system with Spring Data Neo4j by modeling genres and ratings as graph nodes. Learn to build repositories and implement recommendBook logic to fetch and rank books by genre and average ratings.

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Solution

Let’s discuss the solution for implementing a book recommendation system.

Create the Genre class

First, we create the Genre class in the com.smartdiscover.model package.

Java
package com.smartdiscover.model;
import lombok.Data;
import org.springframework.data.neo4j.core.schema.GeneratedValue;
import org.springframework.data.neo4j.core.schema.Id;
import org.springframework.data.neo4j.core.schema.Node;
import org.springframework.data.neo4j.core.support.UUIDStringGenerator;
@Data
@Node("Genre")
public class Genre {
    @Id
    @GeneratedValue(UUIDStringGenerator.class)
    private String id;
    private GenreText genreText;
    public Genre() {
    }
    public Genre(GenreText genreText) {
        this.genreText = genreText;
    }
    public enum GenreText {
        SCIENCE_FICTION,
        FICTION,
        THRILLER,
        ROMANCE,
        FANTASY,
        ADVENTURE,
        BIOGRAPHY,
        SELF_HELP,
        NONFICTION
    }
}

Code explanation:

  • Lines 9–11: We add annotations like @Data and @Node to transform a class into a Neo4j graph node.

  • Lines 13–15: We add the id identifier with the @Id and UUID generator annotations.

  • Lines 22–24: We add the parameterized constructor to accept genreText and create a new Genre object.

  • Lines 26–36: We create the GenreText enum with a few values like SCIENCE_FICTION, FICTION, and ...