Personalized Entity Search by Sparse and Scrutable User Profiles
This addresses the underexplored problem of personalizing general entity search for users, though it is incremental as it builds on existing re-ranking methods.
The paper tackled personalizing book search by using sparse user profiles from online questionnaires, and found that even minimal user information can improve search effectiveness.
Prior work on personalizing web search results has focused on considering query-and-click logs to capture users individual interests. For product search, extensive user histories about purchases and ratings have been exploited. However, for general entity search, such as for books on specific topics or travel destinations with certain features, personalization is largely underexplored. In this paper, we address personalization of book search, as an exemplary case of entity search, by exploiting sparse user profiles obtained through online questionnaires. We devise and compare a variety of re-ranking methods based on language models or neural learning. Our experiments show that even very sparse information about individuals can enhance the effectiveness of the search results.