Date of Award
Bachelor of Science
similarity, song, end
We blend two existing automatic playlist generation algorithms. One algorithm is built to smoothly transition between a start song and an end song (Start-End). The other infers song similarity based on adjacent occurrences in expertly authored streams (EAS). First, we seek to establish the effectiveness of the Start-End algorithm using the EAS algorithm to determine song similarity, then we propose two playlist generation algorithms of our own: the Unbiased Random Walk (URW) and the Biased Random Walk (BRW). Like the Start-End algorithm, both the URW algorithm and BRW algorithm transition between a start song and an end song; however, issues inherent to the Start-End algorithm lead us to believe that our algorithms may create playlists with smoother transitions between songs.
Curbow, James, "Blending Two Automatic Playlist Generation Algorithms" (2016). Honors Theses. 138.