Unlocking the Intelligence of Recommender Systems

The power of Data Science is really growing exponentially every day. The tools of Data Science are helping businesses meet their specific needs and optimizing the processes to help the organizations make clear decisions and to succeed in this, Recommender Systems are playing a crucial role to help Data Scientists.

Recommender Systems are the part of Information Filtering System which seeks a large amount of data to predict the suggestions, preferences or ratings that a customer or user would submit to a product or item. These systems generally produce the output based on the type of input data. The approaches used in Recommender Systems are-

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1. Collaborative Filtering Approach: This method gives the result based on the past behavior of the users. It uses the previous data and history of purchasing or views of the users and then gives the relatable suggestions for the similar decisions that were made by the user. This recommender system model is then applied to the data to predict the products or their ratings according to the past interest of the user.
2. Content-based Filtering Approach: It uses the large series of discrete data characters of a product having some special characteristics to predict the additional recommendations with the same properties. The Keywords of special meaning are used for filtering the same results according to the history of preferences of users and the descriptions of the products. The algorithms used in this approach are commonly based on Information Retrieval and Information Filtering Strategies.

The mechanism to predict the recommendations and ratings are done in a well-structured and logical ways which include Data Collection, Filtering, and Ratings.

Each time a user clicks on an item or link, an event is fired for it and this makes an entry in the database of the system to store the data for future use. The Data is also captured by using the cookies and system sessions browsed by the users. The Filtering is done by applying the algorithms to the type of data. And, the Ratings are done by generally storing the feedback details provided by the users to filter the searches and give the similar suggestions. Some common and widely used Recommender Systems are the recommendations produced by Amazon, Netflix, Pandora, IMDB, Rotten Tomatoes, Spotify, Google News, etc.

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