Exploring the effectiveness of user-driven intent-based recommendation models implemented in a real-world music web service

Abstract This paper explores the effectiveness of a flexible song recommendation function implemented in a music web service. The function allows users to create recommendation models, which we refer to as intent-based recommendation models (IBRMs), according to their intents. For example, a user can develop IBRMs for “cool songs,” “songs for concentrating on work,” and so on, and receive recommendations from each of the IBRMs according to her intents. The key novelty of this work lies in the architecture that enables users to explicitly construct and maintain multiple personalized recommendation models in parallel, each specialized for a particular intent. This user-driven approach contrasts with conventional systems that rely on a single, system-controlled recommendation model per user. To develop an IBRM, the user first initializes it by choosing seed songs and then repeatedly updates it by giving feedback based on whether recommended songs are relevant to the user’s intent. In the case study using the real-world web service “Kiite,” we analyze 1,116 IBRMs created by 417 users and show key characteristics of those IBRMs (e.g., it is meaningful to enable users to create their own IBRMs, because the created IBRMs generate largely different recommendation results from one another). These findings demonstrate the effectiveness and practical value of enabling users to control intent-specific recommendation behavior through the proposed IBRM framework.