Last Updated on by Kumar Raja

Different Approaches In The Recommendation Systems

The ongoing process of global digitization has given a boost to the use of Big Data. Both the e-commerce and retail industries have been highly successful in leveraging the power of data to its full potential. By deploying the Data Analytics techniques in Data Science, the ecommerce & the retail industries are availing numerous benefits in the form of enhanced sales & better customer satisfaction.

Recommender Systems is one of the advanced techniques in Data Science which is making a big difference in the ecommerce & retail industry. Get a clear idea of Recommender Systems & other analytics techniques in the Data Science industry by joining for the best Data Science Training In Hyderabad by Kelly Technologies.

In this blog post, let’s take a look at the different types of approaches in the Recommender Systems

Collaborative Filtering

In this model of Recommendation System, predictions are made based on what might interest a person which is in common with other persons. The functioning of this model is based on the assumption that if a person A likes Kit Kats & person B likes Kit Kat & Perk then person A might also like Perk. 

Content-Based Filtering-

The functioning of this model focuses on the products which users have checked or purchased & recommends products that have similar attributes. The filtering in this model is mainly based on the characteristics of the products, so this model doesn’t rely on the other users to recommend suggestions to a specific user.

Demographic Based Recommender System-

In this type of Recommendation System, recommendations are done based on the demographic classes. The filtering in this model also relies on market research data. This model doesn’t rely on the users rating history.

Knowledge Based Recommender System-

In this type of recommendation system, the recommendations are based on the information in relation to the user’s preferences and needs. By making use of advanced algorithms, this model is capable of drawing connections between a customer’s need and the products that best fit with the needs of the users. 

The other types of recommender systems are hybrid filtering, utility based recommender systems. Work towards building expertise to work on recommendation systems by joining for the best Data Science training program at Kelly Technologies.

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