Recommendation systems are being used by an increasing number of e-commerce sites. They not only make it easier for consumers to find relevant products to purchase, but also help to engage your shoppers, convert them to customers, and increase average order values and sizes.

A personalized product recommendation isn’t based on assumption, a guess or random factors and you probably have seen these type of recommendations more than you can imagine – friend suggestions on Facebook, videos on Youtube based on those previously watched, LinkedIn connections based on mutual connections. The examples are endless. However, what drives the decision-making process in recommendation systems? In short, algorithms.

We thought it would be a great idea to further clarify and define some of the latest terminologies around the most common e-commerce recommendation algorithms and some of the buzzwords used to describe them!

Most of the recommendation systems in e-commerce take either of 3 basic approaches:

Content-based recommendations

In content-based recommendation systems, the buyer expresses some preferences on a set of products and the recommender promotes suggestions based upon a description of the items and profile of user interests. For example, keywords from the product description help the system to retrieve information from a retailer’s catalogue with the items that share common features. If one user is viewing summer dresses or is likely to leave some comments on this section, content-based filtering can use this history to identify and recommend similar content (other dresses or items that match with summer dresses like shoes).

Collaborative filtering recommendations

Opposite the content-based recommendations, collaborative-filtering algorithms generate recommendations based on other customers who are most similar to that user. The algorithm can measure the similarity of multiples customers and make recommendations based upon what certain customers have chosen as relevant. Examples for online retail recommendations would include filters such as “customers who bought this, also bought that” or “customers who looked at this item, also liked this item”. In the simplest form, collaborative filtering works best when data from multiple sources like social media, come together and is sorted into categories. It´s a must for every retailer aiming to personalize their online shopping experience.

Hybrid recommendations

Last but not least, hybrid algorithms for product recommendations are systems that combine multiple recommendation techniques together to achieve a synergy between them. A hybrid approach is great solution for a so-called cold start and information sparsity problem – which refers to a situation when there is no information about the user to relate it to some items in the system.

Due to the huge amount of information available online, the demand for personalization and filtering systems for e-commerce is here to stay. Recommendation systems constitute a specific type of information filtering technique that really helps retailers automate their merchandising efforts and provide an effective form of targeted marketing by creating a personalized shopping experience for each customer.

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