Recommendation based on Deduced Social Networks in an educational digital library

Discovering useful resources can be difficult in digital libraries with large content collections. Many educational digital libraries (edu-DLs) host thousands of resources. One approach to avoiding information overload involves modeling user behavior. But this often depends on user feedback, along with the demographic information found in user account profiles, in order to model and predict user interests. However, edu-DLs often host collections with open public access, allowing users to navigate through the system without needing to provide identification. With few identifiable users, building models linked to user accounts provides insufficient data to recommend useful resources. Analyzing user activity on a per-session basis, to deduce a latent user network, can take place even without user profiles or prior use history. The resulting Deduced Social Network (DSN) can be used to improve DL services. An example of a DSN is a graph whose nodes are sessions and whose edges connect two sessions that view the same resource. In this paper we present a recommendation framework for edu-DLs that depends on deduced connections between users. Results show that a recommendation system built from DSN-dependent parameters can improve performance compared to when only text similarity between resources is used. Our approach can potentially improve recommendation for DL resources when implicit user activities (e.g., view, click, search) are abundant but explicit user activities (e.g., account creation, rating, comment) are unavailable.