Multimodal Topic Learning for Video Recommendation

Understand

Facilitated by deep neural networks, video recommendation systems have made significant advances.

• Existing video recommendation systems directly exploit features from different modalities (e.g., user personal data, user behavior data, video titles, video tags, and visual contents) to input deep neural networks, while expecting the networks to online mine user-preferred topics implicitly from these features.
• However, the features lacking semantic topic information limits accurate recommendation generation.
• In addition, feature crosses using visual content features generate high dimensionality features that heavily downgrade the online computational efficiency of networks.