Characterizing and predicting downloads in academic search

Numerous studies have been conducted on the information interaction behavior of search engine users. Few studies have considered information interactions in the domain of academic search. We focus on conversion behavior in this domain. Conversions have been widely studied in the e-commerce domain, e.g., for online shopping and hotel booking, but little is known about conversions in academic search. We start with a description of a unique dataset of a particular type of conversion in academic search, viz. users' downloads of scientific papers. Then we move to an observational analysis of users' download actions. We first characterize user actions and show their statistics in sessions. Then we focus on behavioral and topical aspects of downloads, revealing behavioral correlations across download sessions. We discover unique properties that differ from other conversion settings such as online shopping. Using insights gained from these observations, we consider the task of predicting the next download. In particular, we focus on predicting the time until the next download session, and on predicting the number of downloads. We cast these as time series prediction problems and model them using LSTMs. We develop a specialized model built on user segmentations that achieves significant improvements over the state-of-the art.