Implicit semantic-based personalized micro-videos recommendation

Abstract

With the rapid development of mobile Internet and big data, a huge amount of data is generated in the network, but the data that users are really interested in a very small portion. To extract the information that users are interested in from the huge amount of data, the information overload problem needs to be solved.In the era of mobile internet, the user's characteristics and other information should be combined in the massive amount of data to quickly and accurately recommend content to the user, as far as possible to meet the user's personalized needs. Therefore, there is an urgent need to realize high-speed and effective retrieval in tens of thousands of micro-videos. Video data content contains complex meanings, and there are intrinsic connections between video data. For multimodal information, subspace coding learning is introduced to build a coding network from public potential representations to multimodal feature information, taking into account the consistency and complementarity information under each modality to obtain a public representation of the complete eigenvalue. An end-to-end reordering model based on deep learning and attention mechanism, called interest-related product similarity model based on multimodal data, is proposed for providing top-N recommendations.The multimodal feature learning module, interest-related network module and product similarity recommendation module together form the new model.By conducting extensive experiments on publicly accessible datasets, the results demonstrate the state-of-the-art performance of our proposed algorithm and its effectiveness.

Multimodal Recommender Systems: A Survey

“…[32], [7] Coarse-grained Attention None [21], [26] Fine-gained Attention None [24] Fine-gained Attention CL [47], [51] User-item Graph None [54] User-item Graph CL [29] Knowledge Graph + Fine-gained Attention None…”

“…VECF [6] performs image segmentation to integrate image features of each patch with other modalities. UVCAN [26] makes image segmentation of video screenshots like VECF and fuse image patches with id information and text information through the attention mechanism, respectively. MM-Rec [52] first extracts the region of interest from the image of news through the target detection algorithm Mask-RCNN, and then fuses POI with news content using co-attention.…”

“…Some models use image processing to denoise. For example, VECF [6] and UVCAN [26] perform image segmentation to remove noise from the image so that they can better model the user's personalized interests. MM-Rec [52] uses a target detection algorithm to select the significant margin of the image.…”