Case Study on Recommendation Engine

Filtering virtual information or data by using recommender algorithms will provide the most relatable and relevant items to the user based on their choices. This set of algorithms are known as recommendation systems or recommender engines. Most tech giants use a few different recommender algorithms that are required to build engines. These algorithms can be simple to complicated and range from simple threshold to black-box neural networks.

Recommendation engines have become important needs for a growing industry which use the information to grow their market share. Also, they can earn a large amount of money by increasing their content or products to the consumer using recommenders. Organizations can also save their investments with the help of recommendation systems.

In the use of the recommender algorithms mainly either “Collaborative Filtering” or “Content-based Filtering” or both are used to set up a commercial recommendation engine. Mostly both the algorithms are effectively used for generating playlists for video or music services, product recommender for any online shopping platform, or content recommender for social media or any blogging platform. The suggestions are generated by the engine to push more content or advertisements to the targeted user with the use of the system.

Content-based systems, compare the content of any items with a user’s profile to recommend useful items to the user. User preferences are vital for this sort of system. Important points to be noted for content-based recommender must be performed are –

• A complete description of items.

• Define user profiles based on their previous engagements.

• Suggest to each user the items matching most with the user profile.

These are the steps that are followed by the algorithm of this system when the recommendation is needed in marketing. For example, suppose a user prefers smart devices. Based on this information, the recommender system will use the data to suggest a quite similar kind of content or product. On that note, the user gets suggestions of similar devices or content according to his/her preferences. Though a content-based engine performs well it has some limitations. That is this recommendation lacks in detecting complex behavior or interdependencies. So, the content-based recommendation even is referred to as Item-User recommendation.

The collaborative system has the most uses in the market. These sorts of recommendations are also based on the preferences of the user. The user’s most liked items are the data for this recommendation. A collaborative engine uses those data to evaluate the market to provide the most relevant products and items to the user. The product user liked in the past will also like in the future, on that basis recommender suggest similar things but with more features that might be liked by the user. The exploitation depends on the behavior of users and items in terms of transaction history, product ratings, selection, and purchasing data. To get more users, connection recommenders use the data of existing users. Collaborative filtering is used to suggest complex items such as movies and so on.

Suppose, a user looked for a specific smartphone brand’s product with good displays and better battery life. Then the collaborative recommender will save the data of searching of the user and use it to suggest a product. The recommended product will be smartphones with good displays and better battery life but the brand will be different from the searched products. This recommendation will change continuously based on the behavior and the searching or liking information. If the behavior change, then the recommendation will also change according to that.

Interactions between the user and the shopping or media content platforms are noted every time it runs. There are no privacy issues with the filtering because the recommendation system gathers data carefully without breaking the barrier of the privacy policies. Companies implement recommender systems to-

• Improve retention to make users more likely to remain loyal.

• Increase sales by doing various research up to 10-50% by recommending accurate products.

• Save time up to 80% for analysts when tailoring suggestions for further research.

• Build strong habits and influence the usage patterns of users.

There are benefits in utilizing the recommendation engine in most businesses and many business cases. An online platform can use recommenders to attract consumers and make a profit. E-commerce, Retail, Media, Banking, Telecom, Utilities and many more platforms should use a recommender engine to increase consumer engagements and make people stay loyal, increase user satisfaction, reduce churn, etc.

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