论文标题
推荐系统的统计实时预测模型
A Statistical Real-Time Prediction Model for Recommender System
论文作者
论文摘要
推荐系统已成为在线购物的不可分割的一部分，随着这些电子商务网站的发展，其可用性正在增加。一个有效，有效的推荐系统对卖方和买方都有利。我们考虑了我们提出的推荐系统中过滤过程的用户活动和产品信息。我们的模型已为2015年Recys Challenge提供的数据集取得了鼓舞人心的结果（大约58％的真实阳性和13％的假阳性）。本文旨在描述一个统计模型，该模型将有助于在会话期间实时预测用户的购买行为。
Recommender system has become an inseparable part of online shopping and its usability is increasing with the advancement of these e-commerce sites. An effective and efficient recommender system benefits both the seller and the buyer significantly. We considered user activities and product information for the filtering process in our proposed recommender system. Our model has achieved inspiring result (approximately 58% true-positive and 13% false-positive) for the data set provided by RecSys Challenge 2015. This paper aims to describe a statistical model that will help to predict the buying behavior of a user in real-time during a session.
