Item-based Collaborative Filtering for Music Recommender System
Blog post from Zilliz
Wanyin App, an AI-based music sharing community, implemented an item-based collaborative filtering (I2I CF) recommender system to sort out music of interest based on users' previous behavior. The system converts songs into mel-frequency cepstrum (MFC), designs a convolutional neural network (CNN) to extract feature embeddings, and uses Milvus as the feature vector similarity search engine for embedding similarity search. This approach helps in generating music recommendations through embedding similarity search and filtering duplicate songs accurately.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| Vector Search | 14 | 58 | 13 | 9 | +38% |
| Kubernetes | 2 | 967 | 124 | 47 | -11% |
| Real-time | 1 | 629 | 236 | 77 | +1% |
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.