Employing Personal Word Embeddings for Personalized Search

Personalized search is a task to tailor the general document ranking list based on user interests to better satisfy the user's information need. Many personalized search models have been proposed and demonstrated their capability to improve search quality. The general idea of most approaches is to build a user interest profile according to the user's search history, and then re-rank the documents based on the matching scores between the created user profile and candidate documents. In this paper, we propose to solve the problem of personalized search in an alternative way. We know that there are many ambiguous words in natural language such as 'Apple', and people with different knowledge backgrounds and interests have personalized understandings of these words. Therefore, for different users, such a word should own different semantic representations. Motivated by this idea, we design a personalized search model based on personal word embeddings, referred to as PEPS. Specifically, we train personal word embeddings for each user in which the representation of each word is mainly decided by the user's personal data. Then, we obtain the personalized word and contextual representations of the query and documents with an attention function. Finally, we use a matching model to calculate the matching score between the personalized query and document representations. Experiments on two datasets verify that our model can significantly improve state-of-the-art personalization models.