Double Standard in Recommendation and Search Algorithms: Popular vs. Niche Artists

Hello everyone,

As a user who actively seeks out new music and values experiencing it in high-fidelity (Hi-Fi), I rely on accurate discovery tools. I frequently use YouTube for music discovery because its recommendation algorithm is highly precise and tailored. When I find a track I want to analyze or enjoy in lossless quality, I switch to Deezer. However, this is exactly where I encounter a deeply frustrating logical flaw in Deezer's algorithm.

I am facing two fundamental issues:

1. Imbalance in the Recommendation System: If I listen to a mainstream or highly popular artist for merely 20-30 seconds (and even skip it), Deezer immediately pushes this artist to my "Recommended" section on the home screen. Conversely, when I discover a new artist in a more specific genre, listen to their track completely 2-3 times in a row, and explicitly hit the "Like" button, they never appear on my home screen. The algorithm seems heavily weighted toward generic popularity metrics rather than my actual interaction data (likes, playtime, and repetition).

2. Inflexible Search Engine: This bias also extends to the search functionality. When I try to search for these liked but less mainstream artists, the system fails to suggest the artist or track until I type the name out exactly, letter by letter. While typing just a few letters is enough for popular names, demanding an exact string match for alternative artists severely degrades the user experience.

A platform offering Hi-Fi audio shouldn't sacrifice a listener's personal, nuanced preferences to mainstream popularity algorithms. The "Like" button and actual listening history must carry more weight than an artist's current chart status.

Are there any planned algorithm updates that will prioritize genuine user interaction data over generic popularity?