# Query Recommendation Using Hybrid Query Relevance
## 2 Related Work  
Much research has explored the area of query recommendation based on query logs [4; 5; 6]. [PERSON] et al. [4] proposed a query suggestion method by constructing struggling flow graph to identify the struggle phrases and mine effective representation based on query log. [PERSON] et al. [6] proposed a  
Figure 1: Architecture showing query recommendation.  
method based on clustering processes in which groups of semantically similar queries are detected. A query log has lots of queries which can be used to understand query search intention. Mining query information in a query log has been discussed. [PERSON] et al. [7] presented query flow graph model (QFG). In a QFG, each node represents a query and each edge between two nodes shows that they are consecutive in a session. Assignment of score values to each query permits use of random walk. It extracts the relationship between queries. However, it has some limitations. On the one hand, only query information is employed in the QFG, but not the URL information and semantic information. The clicked URL in the log and query semantic information can better understand the query semantics and more accurately locate the user's query intention. On the other hand, a query is usually short, with an average of only two or three words. Moreover, some queries are ambiguous. Understanding the semantics of a query and the search intention is limited by the use of only query information in the QFG. [PERSON] et al. [8] proposed novel hierarchical recurrent encoder-decoder architecture to account for sequences of previous queries. The queries in a session is training data, thus making the next query prediction contextual. Among these methods presented, some capture word-level representation [9; 10], some described queries using different feature space [11], some learned the ranking to improve the accuracy of candidate queries [6].  
Clicked URLs are important features to understand query intention. QUBIC [12] was proposed based on a query-URL bipartite graph. It extracts an affinity graph from the initial query-URL bipartite graph only using queries. The weights of edges in an affinity graph are calculated by a query-URL vector, capturing the similarity from query to query. A clustering algorithm [5] was proposed that can automatically mine query major subtopics from the query log, where each subtopic is represented by a cluster containing several URLs and keywords. Nevertheless, these pieces of research are about query-URL pairs for the query recommendation, and are not related to the URL ranking. [PERSON] et al. [13] applied a union matrix which combines query-URL bipartite graph and user-query bipartite graph to learn low-dimensional latent feature vectors of queries and proposed a solution for calculating query similarity using those feature vectors. The query-product clickthrough bipartite graph [14] was proposed by search engine logs and specific domain features such as categories and products popularities. In those approaches above, mining URL information and features can gain query relevance. However, queries submitted to search engines also show the relation between queries. [PERSON] et al. [15] proposed an efficient query suggestion method by calculating the bidirectional transition probability-based query-URL graph and making a strength metric of the query-URL edges. The query log is regarded as the main data in those approaches. However, log files are usually sparse, and there are no edges between many queries and URLs. Therefore, this is not enough for mining query relevance, which only uses a query log.  
Existing work aimed to model query information or query-URL pairs to calculate the query relevance, while our method combines query co-occurrence and query semantics to calculate the relevance-based query information and combine the ranking of URL and query-URL pairs to calculate the relevance-based URL information. At the same time, the query information and URL information are considered to calculate the final query relevance.