Predicting user behaviour on a website is a difficult task, which requires
the integration of multiple sources of information, such as geo-location, user
profile or web surfing history. In this paper we tackle the problem of
predicting the user intent, based on the queries that were used to access a
certain webpage. We make no additional assumptions, such as domain detection,
device used or location, and only use the word information embedded in the
given query. In order to build competitive classifiers, we label a small
fraction of the EDI query intent prediction dataset
\cite{edi-challenge-dataset}, which is used as ground truth. Then, using
various rule-based approaches, we automatically label the rest of the dataset,
train the classifiers and evaluate the quality of the automatic labeling on the
ground truth dataset. We used both recurrent and convolutional networks as the
models, while representing the words in the query with multiple embedding
methods.

本文采用多种嵌入方法以及循环神经网络和卷积神经网络等模型，通过基于查询的分类预测用户意图，从而实现预测用户在网站上的行为，可有效跟踪用户的意图并提供更好的用户体验。

使用 CNN 和 RNN 从搜索查询中预测用户意图

Predicting user intent from search queries using both CNNs and RNNs

Learning a high-dimensional dense representation for vocabulary terms, also
known as a word embedding, has recently attracted much attention in natural
language processing and information retrieval tasks. The embedding vectors are
typically learned based on term proximity in a large corpus. This means that
the objective in well-known word embedding algorithms, e.g., word2vec, is to
accurately predict adjacent word(s) for a given word or context. However, this
objective is not necessarily equivalent to the goal of many information
retrieval (IR) tasks. The primary objective in various IR tasks is to capture
relevance instead of term proximity, syntactic, or even semantic similarity.
This is the motivation for developing unsupervised relevance-based word
embedding models that learn word representations based on query-document
relevance information. In this paper, we propose two learning models with
different objective functions; one learns a relevance distribution over the
vocabulary set for each query, and the other classifies each term as belonging
to the relevant or non-relevant class for each query. To train our models, we
used over six million unique queries and the top ranked documents retrieved in
response to each query, which are assumed to be relevant to the query. We
extrinsically evaluate our learned word representation models using two IR
tasks: query expansion and query classification. Both query expansion
experiments on four TREC collections and query classification experiments on
the KDD Cup 2005 dataset suggest that the relevance-based word embedding models
significantly outperform state-of-the-art proximity-based embedding models,
such as word2vec and GloVe.

本文提出两种具有不同目标函数的学习模型，用于基于查询 - 文档相关性信息学习单词表示，实验结果表明相对于 Word2vec 等基于词语相似度的模型，基于相关性的单词嵌入模型在查询扩展和查询分类等信息检索任务上具有显著优越性。