Why a Recommendation Algorithm Suddenly Makes Everyone an Expert
An online platform for professional articles implements an algorithm that is supposed to give users personalized recommendations based on their previous reading behavior.
The algorithm is trained to favor content with high expert knowledge and technical depth, as these are most appreciated by the most experienced users.
At first, the system works well, but soon it becomes apparent that the algorithm recommends increasingly complex and technically demanding articles to all users – even those without prior knowledge.
Many beginners feel overwhelmed and lose interest, while the algorithm continues to favor the most demanding content.
The team wonders: Why does the recommendation algorithm treat all users as experts, even though this does not correspond to their actual competence?
Question: Why can a recommendation algorithm optimized for the behavior of experienced users lead to recommending complex content to all users, thereby missing the needs of beginners?
Solution follows tomorrow.
Solution
The recommendation algorithm learns which content is particularly well received by experienced users – in this case, complex, technically deep articles.
Since these users interact more frequently with such content, the model weights these articles higher and prioritizes them in the recommendations.
However, the model does not sufficiently distinguish between different user groups with varying levels of knowledge.
As a result, inexperienced users are also recommended the same complex content because the algorithm assumes that this content is generally the most valuable.
Lack of segmentation or personalization according to competence level leads to recommendations that are not suitable for beginners, causing these users to feel overwhelmed or discouraged.
Result: A recommendation algorithm optimized only for the behavior of experienced users can recommend inappropriate content due to insufficient consideration of different user profiles. For a better user experience, algorithms must take into account the diversity of user needs and knowledge and adjust recommendations accordingly to optimally serve both beginners and experts.