Toward Effortless TV-to-Online (T2O) Experience: A Novel Metric Learning Approach

Abstract

Shopping of the same or similar types of products as shown in the online TV programs has been highly desired by many people, especially the youth. To meet this eminent market need, we develop a prototype system to enable effortless TV-to-Online (T2O) experience. A key component of this system is the product search that maps specific items embedded in the video into a list of online merchants. The search performance mainly depends on the estimation of the distance or similarity between the queried item and all the curated items in the database. The simple Euclidean (EU) distance cannot capture the data characteristics, we therefore introduce distance metric learning (DML) to improve the distance estimation. Traditional DML methods only utilize the side information (e.g., similar/dissimilar constraints or relevance/irrelevance judgements) in the target domain, and may fail when the side information is scarce. Transfer metric learning (TML) can be adopted to leverage the side information from related domains. In this paper, we treat each domain equally and propose a novel Ranking-based Heterogeneous Multi-Task Metric Learning (RHMTML) framework, which adopts ranking-based loss, so the learned metric is particularly suitable for search. Extensive experiments demonstrate the effectiveness of our proposed method. We foresee that our approach would transform the emerging T2O trend in both TV and online video market.