标题: TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou 地址:https://arxiv.org/pdf/2407.16357 学校,公司:人大,快手 会议:CIKM 2024 1. 导读 本文主要针对长期兴趣挖掘提出相关的解决方法。以往的方法(SIM和TWIN)通常采用两阶段方法来模拟长期用户行为序列,以...
The framework captures the weights of two user status from the current user historical behavior sequence, models the two different status separately to mine user interests, and combines the captured user status to make more accurate recommendations. In addition, in order to solve the problem that ...
①Title:预测点击率-通过对抗过滤建模长期用户行为序列Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction; ②论文链接:https://arxiv.org/pdf/2204.11587 作者相关 阿里巴巴Lazada团队(LiXiaoChen/ZhangRui + ZhangYu ) 【Lazada和Shopee介绍】:东南亚最大的电商平...
In partic- ular, user behavior sequences (e.g., click, bookmark or purchase of products) are modeled by LSTM and attention mechanism by integrating all the corresponding content, behavior and temporal information. User representations are shared and learned in an end-to-end setting across ...
which fails to fully represent the data. As an alternative, recurrent neural network (RNN)-based methods have recently been trialed to provide overall embeddings of a user behavior sequence. However, these methods can only provide limited information, or aggregated memories of user behavior. ...
User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first regi...
Customer journey visualization: Gain visibility into your different user journeys. You can check individual user paths and view the most common sequence of events completed by users. Retention analysis: Calculateuser retention datausing pre-specified events and compare retention rates over time using a...
At present, sequence-based models have various applications in recommendation systems; these models recommend the interested items of the user according to the user’s behavioral sequence. However, sequence-based models have a limitation of length. When
Cohort analysis:Segment users based on various criteria such as device type, operating system, and custom attributes to uncover behavior patterns unique to different user groups Funnels:Watch how users move through specific sequences in your app to identify drop-off points ...
Hence, when modeling the user’s behavior one can rely on the frequency of occurrence of some carefully selected terms. Moreover, studies conducted both for machine translation and Information Retrieval (IR) tasks have shown that sequences of terms yield superior performance compared to individual ...