A comprehensive artificial intelligence system needs to not only perceive the environment
with different “senses”(eg, seeing and hearing) but also infer the world's conditional (or even …

Among different recommendation techniques, collaborative filtering usually suffer from
limited performance due to the sparsity of user-item interactions. To address the issues …

Sparseness of user-to-item rating data is one of the major factors that deteriorate the quality
of recommender system. To handle the sparsity problem, several recommendation …

Recently, multi-view representation learning has become a rapidly growing direction in
machine learning and data mining areas. This paper introduces two categories for multi …

Collaborative filtering (CF) is a successful approach commonly used by many recommender
systems. Conventional CF-based methods use the ratings given to items by users as the …

Most existing recommender systems focus on modeling the ratings while ignoring the
abundant information embedded in the review text. In this paper, we propose a unified …

While perception tasks such as visual object recognition and text understanding play an
important role in human intelligence, subsequent tasks that involve inference, reasoning …

Data in many real-world applications such as social networks, users shopping behaviors,
and inter-item relationships can be represented as graphs. Graph Neural Networks (GNNs) …

近年来, 社会化推荐系统已成为推荐系统研究领域较为活跃的研究方向之一.
如何利用用户社会属性信息缓解推荐系统中数据稀疏性和冷启动问题, 提高推荐系统的性能 …

Recommendation systems, prevalent in many applications, aim to surface to users the right
content at the right time. Recently, researchers have aspired to develop conversational …