Post by rahim on Jan 31, 2024 4:17:36 GMT -5
Product recommendations with machine learningMay 31, 2023 Google released its machine learning-based product recommendation solution to all users in mid-2020. The Google Recommendations AI is still in a beta stage, but we have already been able to implement it without any major problems. The main advantage of Google's solution is its ability to be integrated into Google's universe, which is supported on the one hand by direct integration into Google's Merchant Center and with a standard tag in Google's tag manager.
At the same time, it is a very open DB to Data solution that allows recommendations for different systems to be displayed on all channels. Building a recommender system How does a recommendation engine work? The aim of every recommendation engine (or recommender system) is to give a user a recommendation that leads them to an action. This can be, for example:To read “another article”. Add a “recommended book” to your shopping cartWatch the “next video”. For this, the recommendation engine needs knowledge about the content or “items” that should be recommended. For products this can be, for example: Product color Manufacturer Price category.
The recommendation engine must have as up-to-date data as possible on this information so that it does not recommend sold-out products, for example. With this data alone, recommendations can now be generated using “content based filtering”. The attempt is usually made to find “similar” items to the one a user is currently looking at. If you want to not only provide recommendations based on your own catalog, but also (also) on the basis of user behavior (reviews, purchases, product views, etc.), the recommendation engine requires additional information about user behavior (the user's events). Basically, the more information the recommendation engine has about the content and user.
At the same time, it is a very open DB to Data solution that allows recommendations for different systems to be displayed on all channels. Building a recommender system How does a recommendation engine work? The aim of every recommendation engine (or recommender system) is to give a user a recommendation that leads them to an action. This can be, for example:To read “another article”. Add a “recommended book” to your shopping cartWatch the “next video”. For this, the recommendation engine needs knowledge about the content or “items” that should be recommended. For products this can be, for example: Product color Manufacturer Price category.
The recommendation engine must have as up-to-date data as possible on this information so that it does not recommend sold-out products, for example. With this data alone, recommendations can now be generated using “content based filtering”. The attempt is usually made to find “similar” items to the one a user is currently looking at. If you want to not only provide recommendations based on your own catalog, but also (also) on the basis of user behavior (reviews, purchases, product views, etc.), the recommendation engine requires additional information about user behavior (the user's events). Basically, the more information the recommendation engine has about the content and user.