The impact of images on user clicks in product search

Abstract

Product search engine faces unique challenges that differ from web page search. The goal of a product search engine is to rank relevant items that the user may be interested in purchasing. Clicks provide a strong signal of a user's interest in an item. Traditional click prediction models include many features such as document text, price, and user information. In this paper, we propose adding information extracted from the thumbnail image of the item as additional features for click prediction. Specifically, we use two types of image features -- photographic features and object features. Our experiments reveal that both types of features can be highly useful in click prediction. We measure our performance in both prediction accuracy and NDCG. Overall, our experiments show that augmenting with image features to a standard model in click prediction provides significant improvement in precision and recall and boosts NDCG.