Sentient AI – Ecommerce Recommendation Engine

An ecommerce recommendation engine is intended to offer a recommendation that is consistent with the goal that the marketer has set when setting up his recommendation tool. On large e-commerce sites, several engines or recommendation algorithms can co-exist, with each providing a specialized function (up-selling, other buyers’ behavior, cross-selling, etc.). The importance of the role of recommendation engines has grown at the same time as online commerce has grown. On the other hand, the goals set by a referral system can be varied.

An ecommerce recommendation engine usually analyzes the frequency of views by a user and the time spent on a page. Indeed, the strategy, the organization and the processes must accompany and frame this technological brick in order to cover a myriad of the use cases. Remember, making recommendations from the ratings of millions of users can have serious implications in terms of performance.

A list of recommendations can be generated even if there is only one user. However, in the case of a large site that manages millions of users and a catalog of thousands of products it is necessary to scan a large number of potential neighbors and that makes real-time recommendation impossible. The stronger the resemblance, the greater the value of the similarity.

We also see that in practice, features and properties, such as the genre of a book or the list of actors in a film, are usually provided by the creators of the book or film and are also offered in an electronic form. When connected to a site, a referral system may offer even more relevant content based on our search history. To make even more compact the list of words present in a document, one can also leave out a certain number of words which are without interest, like the articles, the prepositions, etc.

A referral system may also standardize different variations of the same word. For example, the verb conjugates may be replaced by their infinitive variations. A referral system will be able to accomplish a task only if it has 2 types of information available: 1) a description of the characteristics of the item and 2) a user profile that describes the past interests of the user in terms of an item type preference. For example, it is estimated that nearly a quarter of sales made by Amazon are through recommendations.

Beyond that, the satisfaction of the customer to have found the desired object improves customer loyalty and therefore the advertising that will be done around the site. User preferences frequently remain stable and consistent over time. The engine must arbitrate between the various possible offers, then offer its recommendation to the consumer.


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