We first introduce a factorization framework to tie CF and content-based filtering together. Then we find that the MAP estimation of this framework can be embedded into a multi-view neural network. Through this neural network embedding the framework can be fur...
当涉及到建模协同过滤的关键因素(key factor)———用户和项目(item)特征之间的交互的时候,他们仍然采用矩阵分解的方式,并将内积(inner product)做为用户和项目的潜在特征点乘。通过用神经结构代替内积这可以从数据中学习任意函数,据此我们提出一种通用框架,我们称它为NCF(Neural network-based Collaborative Filtering,基...
【推荐系统论文精读系列】(十一)–DeepFM A Factorization-Machine based Neural Network for CTR Prediction 【推荐系统论文精读系列】(十二)–Neural Factorization Machines for Sparse Predictive Analytics 一、摘要 近年来,深度神经网络在语音识别、计算机视觉和自然语言处理方面取得了巨大...
Systems and techniques are provided for content filtering with convolutional neural networks. A spectrogram generated from audio data may be received. A convolution may be applied to the spectrogram to generate a feature map. Values for a hidden layer of a neural network may be determined based ...
When it comes to sensor networks, the distributed state estimation problem refers to estimating the global state of the system on each node without the need for a central coordination unit [15]. Li et al. [23] presented a neural network control method based on command filtering. Battistelli ...
The convolutional neural network (CNN) model is known for its local connectivity and weight distribution mechanisms, resulting in a reduced number of parameters and faster training. Consequently, numerous studies have been published on sensor-based HAR utilizing CNN10,11. The effectiveness of CNN in...
Recent advances in neural networks have inspired people to design hybrid recommendation algorithms that can incorporate both (1) user-item interaction information and (2) content information including image, audio, and text. Despite their promising results, neural network-based recommendation algorithms po...
A NEURAL NETWORK BASED COLLABORATIVE FILTERING MODEL Recommender systems are one of the business intelligence systems that provide suggestions to the active users for their items purchase in e-commerce store. Most recommender systems use collaborative filtering (CF) or content-based or hyb... P Prabh...
Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation. However, mainstream personalization methods rely on centralized GNN learning on global graph
In this paper, we propose a knowledge-aware attentional neural network (KANN) for dealing with movie recommendation tasks by extracting knowledge entities