Discipline: Data Science
Subcategory: Computer Science & Information Systems
Room: Exhibit Hall
Naomi C Rohrbaugh - University of Pittsburgh
Co-Author(s): Edgar Ceh-Varela, Eastern New Mexico University, Portales NM
Recommender systems (RS) help users to deal with the problem of information overload. These systems suggest items to users based on their preferences. In some real-life situations, the user’s preference for a particular item could be influenced by the entity (e.g., restaurant) that sells or produces the item. For example, an item could be perfectly suited to a user’s preferences but is produced at a venue with poor service or lousy ambiance, aspects that are not to the user’s liking. On the other hand, a venue might have the perfect vibe for a user, but the items produced at that venue do not match the user’s preferences. The recommendation of an item would be improved by considering the user’s preferences for both item and venue. Therefore, the user’s preference for the entity’s characteristics also needs to be considered. This research presents a model for composite recommendations (i.e., recommending items with a “Has-A’ relationship”). The model will account for a user’s preference for both of the related items. As a basis, we use metapath2vec, a graph neural network method, to obtain node embeddings from a heterogeneous graph. This representation learning method maintains the structural relationship of a network with nodes of different types (i.e., user and two different items). The node embeddings for entities with a composite relationship are aggregated during recommendation to account for the user’s preference of these entities. Our proposed model was tested with a real-world dataset collected from BeerAdvocate.com composed of users, breweries, and beers from the state of California, USA. The dataset includes user-beer and user-brewery interactions (i.e., reviews), as well as beer-brewery interactions. The composite recommender system recommends a
Funder Acknowledgement(s): This research was part of an REU program funded by the NSF.
Faculty Advisor: Edgar Ceh-Varela, Eduardo.Ceh@enmu.edu
Role: I began by extracting the beeradvocate dataset using various web-scraping techniques. My research mentor directed me to the metapath2vec method as I was searching for a way to do representation learning with heterogeneous GNNs. I manipulated my dataset into the correct form to use with the metapath2vec algorithm. I got node representations and used them to create a composite recommender system as described in the abstract. My mentor directed me to some good ways to do a feature aggregation, and after trying some out I found a suitable way to implement the composite recommender system. I also implemented a baseline method for comparison. I then tested the system with different parameters to maximize performance. I compared results to the baseline to make my final conclusions about the efficacy of the method. In this project, my mentor directed me to some ideas and helped out when I ran into difficulties. I handled the implementation of the project and made data specific decisions.