After recognizing their need for information, users formulate search queries that reflect their intent.These queries are then submitted to a search engine with the expectation that the results will be relevant and address their information needs.Users rely on the returned results aligning with their intent and context.This highlights the increasing need for advanced retrieval models that can adapt not only to the user's query but also to their broader informational requirements.While significant progress has been made in improving dense retrieval systems to prioritize the most relevant results, few efforts have focused on integrating user history.In this research, we aim to present an approach that incorporates user interaction history, such as clicks and query reformulations, into dense retrievers to achieve more accurate rankings.To measure progress toward this goal, we will utilize the Trip Click benchmark, created from approximately 4M click log entries in the context of a health web search engine.Our objective is to develop a model that enhances dense retrieval results by leveraging user action history, thereby increasing the likelihood that users will find relevant documents more quickly and end their search sessions with greater satisfaction.

Users formulate search queries that reflect an information need. Those queries are then submitted to a search service in the expectation that the retrieved results will allow the user to complete an external task, and align with their broader information context.

Web search services are widely employed for various purposes. After identifying information needs, users attempt to articulate them in web queries that express their intentions. Then, they submit these queries to the chosen search engine with the hope of obtaining relevant results to meet their needs. In some cases, users may not immediately find precisely what they are seeking, prompting them to rewrite the query to obtain a greater number of relevant results or results that are perhaps more related to their intent. While significant work has been done on developing features such as query auto-completion, query suggestion, and query recommendation, the majority of these efforts were based on query co-occurrence or query similarity by clustering them or constructing query flow graphs to capture query connections. These approaches operate under the assumption that frequently observed follow-up queries are more likely to be submitted by users [1, 2, 4].