Recommender systems were conceptualized due to the growing interactions and activities of users on the internet. Online spaces allow users to freely indulge in their favorite activities. For instance, consider IMDB (Internet Movie Database). Binge watchers visit it and click a rating out of 10 to offer their insights on the movie’s quality. This approach gathers feedback through “ratings.” Other metrics for feedback include the browsing of a term which calculates the popularity of a product.

These metrics are used by organizations in recommender systems to determine consumer habits. The objective is to enrich the user experience. In the nomenclature of recommender systems, a product is an “item” and the individual who uses the recommender systems is a “user.”

Recommender systems rely on the concept that there is a strong correlation between the activities of a user and an item. For instance, if a user likes to purchase graphic cards for gaming, then it is more likely that their other activities would be to purchase a gaming mouse, headphones, or any other gaming-related equipment. Thus, their choices can be used to show them more and more relevant recommendations while companies are able to provide unmatched levels of personalization.

Types of Recommender Systems

Recommender systems are classified under the following categories.

Collaborative Filtering

In collaborative filtering, when several users provide their feedback, for instance in the form of ratings, then their “insights” are computed to generate recommendations. The resulting ratings matrices of collaborative filtering are known to be sparse. For instance, suppose there is a movie website in which users assign ratings to reflect their likes/dislikes. Now, if the movie is a popular one, like The Shawshank Redemption, then it is expected to receive a massive number of ratings.

However, each movie has a limited audience. The audience that has seen and rated the movie is grouped as observed or specified ratings while those who have not watched the movie are labeled as unobserved or unspecified ratings.

Collaborative filters work on the foundation that the unobserved ratings may also be imputed. This is done by using the high correlation between items and users. For instance, consider two users Phil and Dan. Both of them like the movies starring Leonardo Di Caprio, and therefore share identical taste. When their ratings get too similar, then the model uses it to calculate their recommendations. This means when there is an item in which only Phil has submitted a rating, then Dan’s rating could be estimated through Phil’s choice and therefore Dan is shown that movie as a recommendation.

Content Based Recommender Systems

When an item’s descriptive characteristics are analyzed and computed for the generation of recommendations, then it is likely that the recommender system is based on content. The term here is “descriptions.” Such systems aggregate the purchasing behaviors and ratings which are recorded from the users and mixed with an item’s description (content).

For instance, considering a movie website, Mark has given a positive rating to the movie “Sixth Sense.” However, data from other users is not available so collaborative filtering is inapplicable. Instead, the recommender system can use content-based methods to go through the description of the movie, like its genre (suspense). Therefore, the system would recommend similar suspense-based movies to the user afterward.

In such systems, the ratings and descriptions of the items are employed as training data for the generation of a regression model or classification (user-specific). Whenever users are assessed, the training data matches their buying history with the description of items.

Knowledge-Based

In scenarios where users are not too active with the purchase of items, knowledge-based systems exist as a useful tool. For instance, when a user buys real estate, then this purchase is commonly long-term. On a similar note, buying automobiles or luxury products are those activities which occur once in a while for a user. As you can understand, there are not enough ratings to offer the users with recommendations. Similarly, the description of such items also changes with the passage of time as the models and versions of these items may also change.

Knowledge-based systems do not consider ratings to generate recommendations. Instead, they attempt to find a link between the item descriptions and the requirements of a user. This approach makes use of “knowledge bases.” Knowledge bases store the similarity functions and set of rules that assist them in retrieving the right item.

For example, if a user intends to purchase a car, then the knowledge-based systems would provide recommendations based on the specifications of the car like the model year of the car, model type (sedan, convertible, hatchback etc), color, price, and other car-related attributes.

Demographic Recommender Systems

At times, the data from the demographics of users is used for designing classifiers which apply a mapping between the purchasing behavior and ratings of the user to their demographics. These systems thrive on the idea that demographic data can prove valuable in the recommendation of items. For instance, a website can show web page recommendations by identifying the interaction of a demographic with a specific web page. Often, a context is added in such systems to further improve their recommendations.

For example, consider a website which provides book recommendations to its users. The user attributes are gathered through an interactive dialog box. The system would account factors like the age, country, language, occupation, status (student/employed/owner), and other relevant factors. For instance, if a user is between the ages of 5-10, then its suggestions would gear towards children books. Likewise, if a user works as a finance analyst, then the system would use to show finance related books. On a similar note, if a user lives in France, then he/she would receive French book recommendations.

Final Thoughts

Recommender systems have become one of the most in-demand applications of machine leaning. Amazon, Netflix, Google, Facebook, every organization uses recommendation system to improve the user experience. Therefore, if you deal extensively with customers, then you might succeed in improving the productivity and efficiency of your business through the installation of recommender systems.