Make a better choice with Recommendation Engines!
With the advent of technology, users don’t need to spend gross amount of time searching for products they need. Imagine a friend sitting next to you, recommending things that surprisingly you like, and you deem it “telepathy” — recommendation engines, on the same note, are a set of technologies aided with advanced intelligence that help with similar suggestions. They produce results on the basis of your last search query, as if they have known your likes and dislikes for a long time. A recommendation engine incorporates a data filtering tool that makes use of certain algorithms and draws out inference after analysis of numerous sources. This technological framework has been incorporated by numerous companies to get a competitive edge in the market.
E-Commerce giants like Amazon and Flipkart have made use of this technology to better serve their customers, for they can bring a user’s desired products right in front of their eyes by providing suggestions based on their search history and of their peer’s as well. People living in the same geographical area might get a set of recommendations popular in that specific region.Today, Amazon with the extensive help of its recommendation engines continues its dominance over online retail. Even the widely popular video streaming platform Netflix has been recommending us to Titles based on the same concept. Statistics indicate that 70% of the users like watching what they receive in their personalized recommendations.
One very popular implementation of Recommendation Engine used world over is Neo4J. The guys over at Neo4J describe it as “as an ACID-compliant transactional database with native graph storage and processing”. Neo4j is the most popular graph database according to DB-Engines ranking, and the 22nd most popular database overall.
So what makes neo4j captivating to the tech giants? Well here’s what:
1. Drivers for popular languages and frameworks
2. Hot Backups
3. Flexible Schema
4. Seamless Data Integration
5. Powerful Cypher Query language
6. Cloud Readiness
7. Scalability and Performance
When talking Neo4J one cannot leave Cypher Queries out of the equation. It is a declarative graph query language that allows for expressive and efficient data querying in a property graph. It was largely an invention of Andrés Taylor while working for Neo4j, Inc. in 2011
It not only protects data integrity of users, but also delivers lightning-fast performance. When the user searches for a specific product, it estimates the predictability of other desired items, on the basis of relationship-based queries, within seconds.The above is achieved via ACID compliance — atomicity, consistency, isolation and durability combined together. When a product is ACID compliant, you have a guarantee that it would carry valid database transactions and that too in a timely manner.
One of the prominent reasons why Neo4J is gaining popularity is because it has an interactive and intuitive user interface. Developers from various start-ups and major industries find it easy to modify the tool as per their requirements. There are plenty of ways to make productivity enhancements, which keep developers interested and thus they even enjoy their work around the configurations. When business requirements change, it takes no time to implement those changes.
When you tap into the power of real-time analytics of Neo4J, fraud rings and other scams can also be avoided. The incursions of fraud teams are prevented by augmentation of discrete data scrutiny. The relationships of users in a fraudulent activity is detected and the security teams match the pace.
There is no doubt that uses of graphs in IT ops are endless. It helps in correlating your network, data center and IT assets to better manage the planning and build innovative products. However, a coin always has two sides.
But before you jump into making use of it in your business, there are a few caveats one must be aware of:
1.Lack of Implementation of Range Indexes
Neo4J has only hash (quick-for-loop-up) indexes. It lacks a range index and sorting can be a ridiculously expensive operation.
2.Lack of built-in distribution system for data
Neo4J supports master-slave replication to use slaves for reads and writes, but each node needs to have a copy of the whole database within it. What this basically translates to is that you are effectively restricted to a single machine running Neo4J.Removal of all non-graphical data out of Neo4J into another server becomes necessary.
Yet, graphs form a fundamental part of the AI systems of modern days. When context and connections are visible on a single platform with the help of a graph there is no telling how much the projects under Artificial Intelligence could be skilled. A company that makes use of Neo4J can seamlessly track all enterprise-level identity and access management (IAM). Thus, the resources within a company’s backend cannot be manipulated. Playing a dominant role in the graph market, recommendation engines connect various communities, customers and partners with graph technology — and users naturally resist peeping into other options.
