Cold-start recommendations
For a first time user of your brand, attempting to provide recommendations without data-sets reflecting interests and tastes can be challenging, this is what we would categorize as a cold-start problem. For this, we suggest implementing our deep content extraction combined with cross-channel pattern recognition and innovative machine learning algorithms. These methods combined provide a solution for extracting personalized recommendations with limited data sets on user tastes & preferences.
Because human behavior is not linear, cross-referencing data with traditional suggestion models seen on most sites with recommendation capability will only yield lackluster results at best. The most efficient and effective method of generating recommendations for users with little to no upfront data is using robust embeddings and algorithms trained specifically to solve the cold-start problem when engaging new users.
These site search engines and filters aid users in completing the singular task of finding a place to stay in a particular city, but are in a deficit when it comes to having algorithms in place to collect user tastes or preferences during their search. In order to accurately recommend hotel rooms or homes that are styled or set-up similarly to places the user has booked before, these companies must find new ways to collect user preferences.
Deploying trained embeddings and algorithms allows us to leverage not only the user-item feedback but the proper content of the items and the noticeable feature of the users simultaneously. These embeddings can be harnessed and used with little to no user feedback.
In addition, by adding layers of collaborative filtering, even more, embeddings are generated by identifying users with similar preferences. This also provides a new & unique solution to the cold-start problem for integrating both new users and new content.
Work with us to improve your user onboarding: