Event-Centric Query Expansion in Web Search (2023)

Abstract: In search engines, query expansion (QE) is a crucial technique to improve search experience. Previous studies often rely on long-term search log mining, which leads to slow updates and is sub-optimal for time-sensitive news searches. In this work, we present Event-Centric Query Expansion (EQE), the QE system used in a famous Chinese search engine. EQE utilizes a novel event retrieval framework that consists of four stages, i.e., event collection, event reformulation, semantic retrieval and online ranking, which can select the best expansion from a significant amount of potential events rapidly and accurately. Specifically, we first collect and filter news headlines from websites. Then we propose a generation model that incorporates contrastive learning and prompt-tuning techniques to reformulate these headlines to concise candidates. Additionally, we fine-tune a dual-tower semantic model to serve as an encoder for event retrieval and explore a two-stage contrastive training approach to enhance the accuracy of event retrieval. Finally, we rank the retrieved events and select the optimal one as QE, which is then used to improve the retrieval of event-related documents. Through offline analysis and online A/B testing, we observed that the EQE system has significantly improved many indicators compared to the baseline. The system has been deployed in a real production environment and serves hundreds of millions of users.

## introduction people are always eager to obtain details and updates on current hot events through search engines. to efficiently return dozens of relevant documents from billions of candidates, most search engines use a "retrieval-rank-rerank-mixed rank"architecture, as illustrated in figure 1 . queries, particularly those relying on keywords, present a tough challenge for query intent understanding due to their brevity, absence of world knowledge, and lack of grammatical structure @xcite . when a significant event takes place, the search intent of users subsequently can rapidly and drastically shift. for example, during the green-poole conflict, a user searching for "green" may be seeking information about the color, while many others desire news about nba player draymond green. while methods based on search log mining @xcite @xcite are still commonly used for query intent understanding, they are limited by their reliance on the accumulation of posterior data and struggle with timely and accurately processing the intent for recent events, making it difficult to retrieve and rank event-related documents. recent approaches @xcite @xcite suggest using additional context from query-associate documents or entities to improve the performance of query understanding. however, they still face challenges in real-time search scenarios. to tackle this challenge, we present eqe, a real-time query expansion system specifically designed to efficiently capture query intent for ongoing events. as depicted in figure 1 , eqe extends the original query with the most fulfilling event, selected from a large pool of candidate events. by performing the same retrieval step with both the original and expanded queries, more results related to the event will be returned. this bypass architecture effectively ensures system flexibility. when there are sufficient machine resources, the number of bypasses can be increased, and multiple query expansions can be used to improve the performance of document retrieval. ## method in this section, we describe our proposed framework of eqe shown in figure 2 . we first introduce the scheme for event collection in industrial scenarios. we then elaborate on event reformulation and semantic retrieval, describing how we use contrastive learning and prompt learning to improve model performance. finally, we discuss online ranking, revealing how to select the optimal expansion. ## system architecture in this section, we describe our baseline and eqe architecture in detail. ## experiments we conduct a series of comprehensive evaluations, both in offline and online environments, incorporating quantitative and qualitative aspects, to prove the advantages of eqe. ## related work query understanding (qu) is a fundamental task of information retrieval (ir), which aims to help reformulate query, predict query intent, and ultimately improve the document relevance modeling @xcite . as an essential method for qu, query expansion (qe) involves the addition of relevant terms or specific information to a query to clarify intention and enhance retrieval performance @xcite . in recent years, most qe methods have been based on word embedding techniques @xcite @xcite @xcite , which select semantically related terms as expansions. usually, word embeddings are learned in two ways, one is based on the semantic vector of terms and the other is based on retrieval relevance @xcite . meanwhile, external data sources, such as wikipedia and wordnet, have also been utilized for qe @xcite . however, these conventional qe methods mainly rely on mining search logs or pre-built ex-pansion libraries, which leads to slow update rates in time-sensitive scenarios. on the other hand, new occurring events in real time can meet the timeliness requirements well, and mining qe from them is a promising research direction. recently @xcite construct a large-scale dataset aiming at extracting event arguments, like subject, predicate and object, from news headlines. however, structured outputs from extractive models @xcite might not be fully utilized by the retrieval and ranking modules. we thus turn to generative models for event reformation. normalized events serve as crucial signals for timesensitive query expansion, which makes the largest contribution to our work. ## conclusion this paper presents our solution for large-scale event-centric query expansion, called eqe, which is able to efficiently capture query intent for ongoing events and help our search engine to retrieve more event-related results. advanced techniques are involved in each stage of eqe to improve performance. offline experiments and online a/b tests verify the effectiveness of eqe. we have deployed the system in tencent qq browser search to serve hundreds of millions of users. meanwhile, we share the design and deployment scheme.