# Query Recommendation Using Hybrid Query Relevance
## 1 Introduction  
As the number of web pages keeps expanding, it is progressively difficult to get useful information which can satisfy user's needs based on original search queries [1]. Thus, users rebuild a new query that is similar to the original search query and is closer to the user's search intentions. We can see some examples in Table 1. For example, when users input a new query \"apple\" to a website, they do not get their useful information. Thus, the search engine will provide a series of new queries, e.g., \"apple website\" and, \"iPhone\". In such a way, users can choose a new query to search relevant information and quickly get what they want. Input queries are usually too short and ambiguous to express the true idea. So, understanding the query and mining intention are the key steps.  
A query log [2] is an important resource to mine user search behavior. The user submits a query to a search engine that leads to a series of information in the query log. The sequences of queries issued by a user within a short time have same intention. A session is defined as sequences of queries that are submitted to satisfy the same intention. Therefore, query co-occurrence in the same session has query relevance and can be used to produce a recommendation. Only using query co-occurrence is prone to data sparsity and loss of much useful information in a query log.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Query** & **Recommended Queries** \\ \hline apple & apple website, apple hk, iphone xr, apple watch4 \\ book & table, pencil box, study, desk \\ basketball & basketball player, basketball court, soccer \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of query recommendation.  
Clicked URLs in a query log have been used in query recommendation [3]. Clicking behaviors show query intention, to some extent. For instance, when the user submits the initial query \"apple\" to the search engine, its search aims to find the \"iPhone official website\". If the URL of \"Apple's official offer\" clicked by the user is considered in the query recommendation, the next recommended query will have the information of iPhone, which is closer to the user's real search intention. Accordingly, the clicked URLs can reveal the user's search intention. Query semantics is also an important factor to understand queries. Both query information and URL information are based on counting the number, and lacking query semantics. To better understand query intention, we propose a query recommendation method. The model is shown in Figure 1.  
In our model, we mine query co-occurrence from query log and use query semantics to calculate query relevance. At the same time, we calculate the query relevance by query-URL pairs, adding the ranking of URLs. We can obtain hybrid query relevance, combining the relevance of query information and URL information.  
The three major contributions of this paper are summarized as follows:  
1. [label=(0)]
2. Solely mining query information from a query log can obtain little useful information and cause data sparsity. Therefore, we use the corpus to train a query embedding vector, getting query semantics to expand the query information and improving the accuracy of the relevance between queries.
3. We combine the number of clicked URLs and the ranking of URLs in the web pages to calculate query relevance. The two different queries are more similar when they have the numbers of the same clicked URLs. At the same time, the ranking of the URL in the web page is higher; the URL is more related to the query.
4. We calculate the hybrid query relevance by query information and URL information. Queries in a session have same query intention. The clicked URLs can more accurately understand query intention. Comprehensive consideration of the query information and query-URL pairs is an effective way to understand the user's intention.