在RecSys 2021上实现协同：使用快速神经模型预测Twitter用户参与度
Synerise at RecSys 2021: Twitter user engagement prediction with a fast neural model
摘要：
在本文中，我们提出了我们的第二位解决方案，以ACM RecSys 2021挑战由Twitter组织。这项挑战旨在预测一组推文的用户参与度，提供一个非常大的数据集，其中包括从超过四周的真实Twitter交互中采样的10亿个数据点。每个数据点包含多个信息源，例如推文文本以及参与特征、用户特征和推文特征。该挑战通过在模型评估阶段引入严格的延迟约束，使问题接近真实的生产环境：在具有64 GB内存的单个CPU核心上，单个tweet参与预测的平均推理时间限制为6 ms。我们提出的模型依赖于广泛的特征工程执行的方法，如有效流形密度估计（EMDE）-我们以前介绍的算法的基础上局部敏感哈希方法，和新的傅立叶特征编码，等等。总的来说，我们创建了许多描述用户Twitter帐户状态和推文内容的功能。为了遵守严格的延迟约束，底层模型是一个简单的残差前馈神经网络。该系统是我们以前的方法的变体，在2021年KDD杯，2021年WSDM挑战赛和2020年SIGIR eCom挑战赛中取得了成功。我们发布源代码：https://github.com/Synerise/recsys-challenge-2021
英文摘要：
In this paper we present our 2nd place solution to ACM RecSys 2021 Challenge organized by Twitter. The challenge aims to predict user engagement for a set of tweets, offering an exceptionally large data set of 1 billion data points sampled from over four weeks of real Twitter interactions. Each data point contains multiple sources of information, such as tweet text along with engagement features, user features, and tweet features. The challenge brings the problem close to a real production environment by introducing strict latency constraints in the model evaluation phase: the average inference time for single tweet engagement prediction is limited to 6ms on a single CPU core with 64GB memory. Our proposed model relies on extensive feature engineering performed with methods such as the Efficient Manifold Density Estimator (EMDE) - our previously introduced algorithm based on Locality Sensitive Hashing method, and novel Fourier Feature Encoding, among others. In total, we create numerous features describing a user's Twitter account status and the content of a tweet. In order to adhere to the strict latency constraints, the underlying model is a simple residual feed-forward neural network. The system is a variation of our previous methods which proved successful in KDD Cup 2021, WSDM Challenge 2021, and SIGIR eCom Challenge 2020. We release the source code at: https://github.com/Synerise/recsys-challenge-2021
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
