Video Recommendation System: Framework, Topic Clustering, and Related Video Retrieval
The paper proposes a video recommendation framework that combines recall and ranking modules, using a multi‑modal topic clustering approach—integrating audio, visual, and textual features via NeXtVLAD, PCA, and K‑Means—to generate unified video representations, improve candidate selection, and boost click‑through and viewing time, while addressing cold‑start and semantic relevance challenges.
