Sentiment Analysis and Social Computing
SHANG Qi, ZENG Biqing, WANG Shengyu, ZHOU Caidong, ZENG Feng
2018, 32(11): 86-96.
The sparseness of rating data is one of the main factors that affect the recommender models prediction. To exploit the advantage of convolutional neural networks in feature extraction and attention mechanism in feature selection, a probability matrix factorization model (PMF) with attention convolutional neural network(ACNN) is proposed as attention convolutional model based matrix factorization (ACMF). Firstly, the ACMF model compresses the high dimensional and sparse word vectors into low dimensional and dense feature vectors through word embedding technique. Then, it uses the local attention layer and convolutional layer to learn the feature of review document, and utilizes the user and item’s latent models to reconstruct the rating prediction matrix. Finally, the loss function is set as the root-mean-square error of rating matrix. Compared with the best prediction model PHD, the ACMF model increases the accuracy rate on ML-100k, ML-1m, ML-10m and Amazon datasets by 3.57%, 1.25%, 0.37% and 0.16%, respectively.