Building a Graph-based Recommendation System with Milvus, PinSage, DGL, and MovieLens Datasets

This article explains how to build a graph-based recommendation system using open-source tools such as Milvus, PinSage, and DGL. Recommendation systems are algorithms that make relevant suggestions to users based on their preferences and behaviors. Two common approaches to building recommendation systems are collaborative filtering and content-based filtering. In this example, the author uses the MovieLens datasets to build a user-movie bipartite graph for classification purposes. The PinSage model is then used to generate embedding vectors of pins as feature vectors of the acquired movie information. These embeddings are loaded into Milvus, which returns corresponding IDs and enables vector similarity search. Finally, the system recommends movies most similar to user search queries.

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.