摘要时间序列是按照时间排序的一组随机变量, 它通常是在相等间隔的时间段内依照给定的采样
率对某种潜在过程进行观测的结果. 时间序列数据本质上反映的是某个或者某些随机变量随时间 …

We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback.
This non-linear probabilistic model enables us to go beyond the limited modeling capacity of …

Given e-commerce scenarios that user profiles are invisible, session-based
recommendation is proposed to generate recommendation results from short sessions …

RNNs have been shown to be excellent models for sequential data and in particular for data
that is generated by users in an session-based manner. The use of RNNs provides …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

Stock prediction aims to predict the future trends of a stock in order to help investors make
good investment decisions. Traditional solutions for stock prediction are based on time …

Recurrent neural networks (RNNs) were recently proposed for the session-based
recommendation task. The models showed promising improvements over traditional …

Most real-world recommender services measure their performance based on the top-N
results shown to the end users. Thus, advances in top-N recommendation have far-ranging …

Sequential location recommendation plays an important role in many applications such as
mobility prediction, route planning and location-based advertisements. In spite of evolving …

Most data for evaluating and training recommender systems is subject to selection biases,
either through self-selection by the users or through the actions of the recommendation …