Abstract:
In auction-based systems such as in used car centers and online auction websites, users usually bid on items, and those items get sold to their highest bidders. In these settings, every item is unique and can be sold only once, which means users’ purchase histories will be unique, and no common items will exist across them. On the other hand, items will not have any historical sales at all. Such extreme settings pose a significant challenge to the current recommender systems models that rely on historical user-item interactions. While some of those models will not be applicable altogether, such as the matrix factorization models, neighborhood models, and even the naive most-popular model, the rest will need to rely only on items’ attributes. In this paper, we address the challenges of auction-based item recommendation by proposing a simple multi-relational recommender model (MultiRec) that can seamlessly leverage user and item attributes along with auxiliary relational information such as the user’s bidding history. Experiments on one proprietary dataset from Volkswagen Financial Services used-cars center, and on a real-world publicly available eBay dataset show that the proposed model significantly outperforms multiple state-of-art models in the task of auction-based unique item recommendation.
