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In this paper, we investigate the task of aggregating search results fromheterogeneous sources in an E-commerce environment. First, unlike traditionalaggregated web search that merely presents multi-sourced results in the firstpage, this new task may present aggregated results in all pages and has todynamically decide which source should be presented in the current page.Second, as pointed out by many existing studies, it is not trivial to rankitems from heterogeneous sources because the relevance scores from differentsource systems are not directly comparable. To address these two issues, wedecompose the task into two subtasks in a hierarchical structure: a high-leveltask for source selection where we model the sequential patterns of userbehaviors onto aggregated results in different pages so as to understand userintents and select the relevant sources properly; and a low-level task for itempresentation where we formulate a slot filling process to sequentially presentthe items instead of giving each item a relevance score when deciding thepresentation order of heterogeneous items. Since both subtasks can be naturallyformulated as sequential decision problems and learn from the future userfeedback on search results, we build our model with hierarchical reinforcementlearning. Extensive experiments demonstrate that our model obtains remarkableimprovements in search performance metrics, and achieves a higher usersatisfaction.

Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning