Example: Academic Search PaperID: 1770 Cross-domain Ranking via Latent Space Learning Jie Tang *+ and Wendy Hall ‡ *Department of Computer Science and Technology, Tsinghua University †Tsinghua National Laboratory for Information Science and Technology (TNList) ‡Electronics and Computer Science, University of Southampton, UK Cross-domain Ranking: learning to rank objects from multiple interrelated domains. Also called heterogeneous cross-domain ranking, when the domains are different. Problem Formulation Example: Academic Search Input: Given T domains, with each having a training dataset—i.e., ranking pairs {(xt1q, yt1q), …, (xtnq, ytnq)}, where xt1q is K-dimensional feature vector to represent the relevance between an object in domain t and the query q at time t; ytnq ∈{r1, …, rlt}represents the ranking level of the object w.r.t. the query q; Learning: learn T ranking functions {ft}Tt=1 simultaneously, with each ft for predicting the rank level of unlabeled objects in the corresponding domain. BayCDR: Cross-domain Ranking via Latent Space and Bayesian Learning Basic model The proposed model Learning algorithm Results Accuracy performance Scalability performance Dataset: LETOR 2.0 (Liu et al. 2007): a public dataset for learning to rank research. LETOR consists of three homogeneous sub datasets (i.e., TREC2003, TREC2004, and OHSUMED), with 50, 75, and 106 queries, respectively. AMiner (Tang et al. 2008): The dataset contains 14,134 authors, 10,716 papers, and 1,434 conferences. Given a query, the goal is to find experts, top conferences, and authoritative papers for the query. Jie Tang and Wendy Hall. Cross-domain Ranking via Latent Space Learning. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17).