The Future of Recommender Systems in Music and Movies

Introduction

This blog explores my ideal scenes for recommender systems in music and movies. As AI, especially Large Language Models (LLM) continues to advance, these systems have the potential to revolutionize how we interact with different devices. Here, I will present AI’s current capabilities, future developments, and the importance of user control, privacy, and transparency.

Recommender Systems Today

In one sentence: Recommender systems have been excellent at providing personalized suggestions by great algorithms, but some of them lack interactive user engagement.

Currently, recommender systems operate behind the scenes, delivering tailored content based on user preferences and behaviors. While they excel at making appropriate and satisfying recommendations, there is a growing need for these systems to become more interactive and user-friendly.

For Music Platforms Like NetEase Music, Spotify

Spotify’s AI Playlist: A Step Forward

Spotify’s AI Playlist is a significant advancement in AI-driven music recommendations. It demonstrates how AI can curate playlists that resonate with individual tastes.

(… I will add more about music platforms.)

Enhancing User Control

In one sentence: Music platforms should empower users with more control over their recommendations.

Allowing users to have greater control over the system can enhance their experience. For instance, users should be able to communicate with assistants (if they have) to refine their music preferences actively.

Interactive Assistants

In one sentence: Integrating conversational assistants can transform how users interact with music platforms.

Imagine asking your intelligent speaker for a playlist of chilling music, and it responds with personalized recommendations based on your listening history. This dynamic interaction can make the music experience more engaging and tailored to individual moods and preferences.

Dynamic Playlists

In one sentence: Music platforms should move beyond static playlists to offer more dynamic, personalized options.

Platforms could leverage AI to create dynamic playlists that evolve based on user interactions. For example, a user might say, “Play some relaxing music by my favorite artists,” and the system can craft a playlist from a cache of preferred songs, ensuring a fresh and personalized listening experience every time.

For Movies

Embracing AR/VR

In one sentence: Integrating AR/VR can provide a more immersive movie recommendation experience.

With the rise of AR/VR headsets like the Apple Vision Pro, movie platforms have an opportunity to present recommendations in a more immersive manner. Imagine watching a trailer in 360 degrees, feeling as though you are part of the scene. This multi-modal approach can make movie discovery more engaging and exciting.

Analyzing User Inputs

In one sentence: Personalized movie recommendations should be able to understand and respond to complex user inputs.

Advanced language models can analyze users’ spoken or written requests to provide more tailored recommendations. Whether it’s a time-specific request like “Show me a good movie for a Sunday afternoon” or a scene-related one, these systems should be capable of delivering precise and personalized suggestions.

The Future: Universal Recommender Systems, in my own mind

In one sentence: Future recommender systems will understand and respond to both audio and text inputs to deliver highly personalized experiences.

As language models become more sophisticated and cost-effective, the potential for universal recommender systems grows. These systems could seamlessly interpret user inputs in various formats, making personalized recommendations more intuitive and accessible. Imagine if they can analyze your mood from your voice and recommend a song or movie that suits your current state of mind.

(Add more about the future of recommender systems.)