论文标题
基于会话建议的多对一的经常性神经网络
Many-to-one Recurrent Neural Network for Session-based Recommendation
论文作者
论文摘要
本文介绍了D2KLAB团队对2019年Recsys Challenge的方法，该方法重点介绍了根据用户会议推荐住宿的任务。一个人说“酒店的房间很大，员工友好而高效”的人是什么感觉？这是积极的。与句子中的单词顺序相似，在该句子中可以肯定这种感觉是什么，分析用户在网站中执行的一系列操作可以预测用户在购物会议结束时将添加到他的篮子中的项目。我们建议使用多对一的复发性神经网络，该网络了解用户将根据他在浏览会话中执行的操作顺序单击住宿的可能性。更具体地说，我们将基于规则的算法与封闭式复发单元RNN相结合，以便对显示给用户显示的可容纳列表进行排序。我们在验证集中优化了RNN，调整了超参数，例如学习率，批处理大小和适应性嵌入尺寸。与情感分析任务的类比给出了令人鼓舞的结果。但是，在训练阶段，它在计算阶段的要求很高，需要进一步调整。
This paper presents the D2KLab team's approach to the RecSys Challenge 2019 which focuses on the task of recommending accommodations based on user sessions. What is the feeling of a person who says "Rooms of the hotel are enormous, staff are friendly and efficient"? It is positive. Similarly to the sequence of words in a sentence where one can affirm what the feeling is, analysing a sequence of actions performed by a user in a website can lead to predict what will be the item the user will add to his basket at the end of the shopping session. We propose to use a many-to-one recurrent neural network that learns the probability that a user will click on an accommodation based on the sequence of actions he has performed during his browsing session. More specifically, we combine a rule-based algorithm with a Gated Recurrent Unit RNN in order to sort the list of accommodations that is shown to the user. We optimized the RNN on a validation set, tuning the hyper-parameters such as the learning rate, the batch-size and the accommodation embedding size. This analogy with the sentiment analysis task gives promising results. However, it is computationally demanding in the training phase and it needs to be further tuned.
