Transcription of Product-based Neural Networks for User Response Prediction
{{id}} {{{paragraph}}}
Example: tourism industry
Product-based Neural Networks for User Response Prediction
Product-based Neural Networks for User Response Prediction Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu Ying Wen, Jun Wang Shanghai Jiao Tong University University College London {kevinqu, hcai, kren, wnzhang, { , Abstract Predicting user responses, such as clicks and con- highly depend on feature engineering in order to capture high- [ ] 1 Nov 2016. versions, is of great importance and has found its usage in order latent patterns [7]. many Web applications including recommender systems, web Recently, deep Neural Networks (DNNs) [8] have shown search and online advertising. The data in those applications is mostly categorical and contains multiple fields; a typical great capability in classification and regression tasks, including representation is to transform it into a high-dimensional sparse computer vision [9], speech recognition [10] and natural binary feature representation via one-hot encoding.}}
where yis the ground truth (1 for click, 0 for non-click), and y^ is the predicted CTR of our model as in Eq. (1). B. Inner Product-based Neural Network In this section, we demonstrate the Inner Product-based Neural Network (IPNN). In IPNN, we firstly define the pair-wise feature interaction as vector inner product : g(f i;f j) = hf i;f ji.
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: