Multilingual question answering over knowledge graph (KGQA) aims to derive answers from
a knowledge graph (KG) for questions in multiple languages. To be widely applicable, we …

Cross-Domain Recommendation (CDR) has attracted increasing attention in recent years as
a solution to the data sparsity issue. The fundamental paradigm of prior efforts is to train a …

Embedding & MLP has become a paradigm for modern large-scale recommendation
system. However, this paradigm suffers from the cold-start problem which will seriously …

Finding node correspondence across networks, namely multi-network alignment, is an
essential prerequisite for joint learning on multiple networks. Despite great success in …

Answering complex queries on knowledge graphs is important but particularly challenging
because of the data incompleteness. Query embedding methods address this issue by …

Monolingual word alignment is crucial to model semantic interactions between sentences. In
particular, null alignment, a phenomenon in which words have no corresponding …

Sufficient dimension reduction is used pervasively as a supervised dimension reduction
approach. Most existing sufficient dimension reduction methods are developed for data with …

Semi-supervision is a promising paradigm for Bilingual Lexicon Induction (BLI) with limited
annotations. However, previous semisupervised methods do not fully utilize the knowledge …

Bilingual lexicons form a critical component of various natural language processing
applications, including unsupervised and semisupervised machine translation and …

Site recommendation, which aims at predicting the optimal location for brands to open new
branches, has demonstrated an important role in assisting decision-making in modern …