Recommender System

Do you want to be able to do, what Amazon does?

Amazon, Zalando, YouTube - these successful companies have one thing in common: they have an intelligent recommendation system. A software system that predicts how strong a customer's interest in a product is. With this knowledge, they can automatically generate personalized product recommendations for each customer which match their personal interests and needs. As a source of information for the prediction, the system uses product data and customer attributes, as well as the search and purchase history of the customer and similar customers.

Task: Austria`s leading online job platform is constantly aiming at making it easier for users to search for jobs or employees. For this purpose, a specific recommendation system should be developed and implemented which would display personal job recommendations to every job seeker.

Our approach: Through our sound expertise in the fields of recommender systems we developed a job recommendation system by using artificial intelligence. Therefor several methods such as Collaborative Filtering, Graph-Based Algorithms, and the Natural Language Analysis (NLP) have been combined to achieve the highest possible degree of personalization.

Result: The recommendation system of 7LYTIX automatically determines the preferences of a job seeker such as position, area, industry, place of work or company size. Therefor no explicit user input or filter settings are required. Already after his first search query the user receives personalized job recommendations which get more and more accurate during the ongoing search.

Benefit: By offering its users a significantly extended service that saves effort, time and money, the leading online career portal karriere.at is further increasing its competitive edge. For applicants, the process of online job search is now much more convenient. But also advertising companies face the advantage that they are more likely to reach the right candidates faster with their job ads.

Advantages: Simplified job search and increased success of job ads.

Summary
Development of a self-learning recommender system which predicts the interest of a user for a job ad and simultaneously displays ads which match the user’s preferences.