Hybrid Recommender System

With the development of technology, our habits are also changing. As such, most of today’s E-Commerce sites use their own proprietary recommendation algorithms to better serve customers with the products they have to like. There are many examples such as Netflix’s movies, Spotify’s music, Facebook recommending friends, product recommendations of Amazon, etc. One of the reasons why these companies are so popular can be shown that their business structures are based on recommendation systems.

What is Recommendation System?

A recommender system, or a recommendation system, can be thought of as a subclass of information filtering system that seeks to predict the best “rating” or “preference” a user would give to an item which is typically obtained by optimizing for objectives like total clicks, total revenue, and overall sales.

  • Attribution Information, about each user and items
  • Content-Based Filtering
  • Hybrid Recommendation Systems

Collaborative Filtering Methods

These types of models use the collaborative power of the ratings provided by multiple users to make recommendations and rely mostly on leveraging either inter-item correlations or inter-user interactions for the prediction process. Intuitively, it relies on an underlying notion that two users who rate items similarly are likely to have comparable preferences for other items.

Content-Based Filtering Methods

In these types of systems, the descriptive attributes of items/users are used to make recommendations. The term “content” refers to these descriptions. In content-based methods, the ratings and interaction behavior of users are combined with the content information available in the items.

Hybrid Methods

In many cases, a wider variety of inputs is available; in such cases, many opportunities exist for hybridization, where the various aspects from different types of systems are combined to achieve the best of all worlds. The approach is comparable to the conventional ensemble analysis approach, where the power of multiple types of machine learning algorithms is combined to create a more robust model.

Dataset and Story

MovieLens, a movie recommendation service, provided the dataset. It contains the rating scores for these movies along with the movies.

  • title — Movie name
  • movieId — Unique movie number. (UniqueID)
  • rating — The rating given to the movie by the user
  • timestamp — Evaluation date

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