融合内容特征和时序信息的深度注意力视频流行度预测模型

针对视频流行度动态变化过程中的时序信息难以捕捉的问题，提出一种融合内容特征和时序信息的深度注意力视频流行度预测模型（DAFCT）。首先，根据用户的反馈信息，构建基于注意力机制的长短期记忆网络（Attention-LSTM）模型来捕捉流行趋势并挖掘时序信息；然后，采用神经网络因子分解机（NFM）处理多模态的内容特征，并采用嵌入技术对稀疏的高维特征进行降维处理，从而降低模型的计算复杂性；最后，采用concatenate方法融合时序信息和内容特征，并设计了一种深度注意力视频流行度预测（DAVPP）算法来求解DAFCT。实验结果表明，与Attention-LSTM模型和NFM模型相比，DAFCT的召回率分别提高了10.82和3.31个百分点，F1分数分别提高了9.80和3.07个百分点。

Deep attention video popularity prediction model fusing content features and temporal information

Aiming at the problem that it is difficult to capture the temporal information during the dynamic change of video popularity, a Deep Attention video popularity prediction model Fusing Content and Temporal information (DAFCT) was proposed. Firstly, according to the users' feedback information, an Attention mechanism based Long Short-Term Memory network (Attention-LSTM) model was constructed to capture the popular trend and mine the temporal information. Secondly, Neural Factorization Machine (NFM) was used to process multi-modal content features and embedding techniques were adopted to reduce the computational complexity of the model by reducing the dimension of sparse high-dimensional features. Finally, the concatenate method was employed to fuse the temporal information and content features, and a Deep Attention Video Popularity Prediction (DAVPP) algorithm was designed to solve the proposed DAFCT. Experimental results show that compared with Attention-LSTM model and NFM model, the recall of DAFCT is improved by 10.82 and 3.31 percentage points, and the F1 score was improved by 9.80 and 3.07 percentage points, respectively.