The general aim of the recommender system is to provide personalized suggestions to
users, which is opposed to suggesting popular items. However, the normal training …

Hybrid data combining both tabular and textual content (eg, financial reports) are quite
pervasive in the real world. However, Question Answering (QA) over such hybrid data is …

Recommender systems usually amplify the biases in the data. The model learned from
historical interactions with imbalanced item distribution will amplify the imbalance by over …

Short text classification has been widely explored in news tagging to provide more efficient
search strategies and more effective search results for information retrieval. However, most …

Automatic tagging on software information sites is a tag recommendation service. It aims to
recommend content-based tags for a software object to help developers make distinctions …

The integration of deep learning (DL) and fuzzy learning (FL) is considered a recently
emerging and promising research direction in data embedding. The integrated fuzzy neural …

Existing methods towards outfit compatibility modeling seldom explicitly consider multimodal
correlations. In this work, we explore the consistent and complementary correlations for …

Multi-label datasets often contain label information with missing values and recovering them
is a non-trivial challenge. Several methods augment the observed label matrix by …

Video moment localization, as an important branch of video content analysis, has attracted
extensive attention in recent years. However, it is still in its infancy due to the following …

Traditional recommenders suffer from hidden confounding factors, leading to the spurious
correlations between user/item profiles and user preference prediction, ie, the confounding …