Abstract
With the explosion of web information, search engines have become main tools in information retrieval. However, most queries submitted in web search are ambiguous and multifaceted. Understanding the queries and mining query intention is critical for search engines. In this paper, we present a novel query recommendation algorithm by combining query information and URL information which can get wide and accurate query relevance. The calculation of query relevance is based on query information by query co-concurrence and query embedding vector. Adding the ranking to query-URL pairs can calculate the strength between query and URL more precisely. Empirical experiments are performed based on AOL log. The results demonstrate the effectiveness of our proposed query recommendation algorithm, which achieves superior performance compared to other algorithms.
Highlights
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]
The query recommendation algorithm is based on hybrid query relevance which combines query relevance-based query information and query relevance based on URL information
The query relevance based on query information can be obtained as follows: Relquery(qi, qj) = αRelsession(qi, qj) + (1 − α)Relsem(qi, qj) where Relquery(qi, qj) denotes query relevance based on query information, Relsem(qi, qj) denotes the relevance which calculated by query semantics, Relsession(qi, qj) denotes the relevance where queries in same session, and α denotes weight
Summary
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]. When users input a new query “apple” to a website, they do not get their useful information. 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. The user submits a query to a search engine that leads to a series of information in the query log. If the URL of “Apple’s official offer” clicked by the user is considered in the query recommendation, the recommended query will have the information of iPhone, which is closer to the user’s real search intention. We mine query co-occurrence from query log and use query semantics to calculate query relevance. Comprehensive consideration of the query information and query-URL pairs is an effective way to understand the user’s intention
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