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Due to the easy access, implicit feedback is often used for recommender systems. Compared with point-wise learning and pair-wise learning methods, list-wise rank learning methods have superior performance for top-\(N\) recommendation. Recent solutions, ...

Graph neural networks (GNNs) have gained significant attention for their impressive results on different graph-based tasks. The essential mechanism of GNNs is the message-passing framework, whereby node representations are aggregated from local ...

The exponential availability of protein sequences has led to the dominance of the pretraining-then-finetuning paradigm for protein function prediction. However, finetuning a pretrained protein language model for diverse downstream tasks requires ...

Recommender systems have become an important instrument to connect people to information. Sparse, complex, and rapidly growing data presents new challenges to traditional recommendation algorithms. To overcome these challenges, various deep learning-based ...

Open-vocabulary detection (OVD) aims to identify objects from novel, unseen categories that extend beyond the base categories encountered during training. Recent approaches generally resort to large-scale Visual-Language Models (VLMs), such as CLIP, to ...

Coarse-graining (CG) is a widely used technique for simplifying complex molecular systems in molecular dynamics simulations. It involves mapping fine-grained (FG) atoms to several CG beads, which enables the study of larger systems and longer simulation ...

The infusion of external knowledge bases into IR models can provide enhanced ranking results and greater interpretability, offering substantial advancements in the field. The first workshop on Knowledge-Enhanced Information Retrieval (KEIR @ ECIR ...

Knowledge graph embedding (KGE) is an increasingly popular technique that aims to represent entities and relations of knowledge graphs into low-dimensional semantic spaces for a wide spectrum of applications such as link prediction, knowledge reasoning ...

Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic ...

A high-dimensional knowledge graph embedding (KGE) space is usually required for a better reasoning capability in representing entities and relations. However, a high-dimensional KGE can lead to high memory overhead with the increasing number of ...

Multimodality Representation Learning, as a technique of learning to embed information from different modalities and their correlations, has achieved remarkable success on a variety of applications, such as Visual Question Answering (VQA), Natural ...

In this article, we study the problem of dynamic personalized product search. Due to the data-sparsity problem in the real world, existing methods suffer from the challenge of data inefficiency. We address the challenge by proposing a Dynamic Bayesian ...

Traditional methods of pre-training, fine-tuning, and ensembling often overlook essential relational data and task interconnections. To address this gap, our study presents a novel approach to harnessing this relational information via a relational graph-...

Graph neural networks (GNNs) have become the de facto standard for representational learning in graphs, and have achieved state-of-the-art performance in many graph-related tasks; however, it has been shown that the expressive power of standard GNNs are ...

This paper focuses on continual meta-learning, where few-shot tasks are heterogeneous and sequentially available. Recent works use a mixture model for meta-knowledge to deal with the heterogeneity. However, these methods suffer from parameter ...

Top-N recommendation is a common tool to discover interesting items, which ranks the items based on user preference using their interaction history. Implicit feedback is often used by recommender systems due to the hardness of preference ...

Conversational Recommendation Systems (CRSs) aim to improve recommendation performance by utilizing information from a conversation session. A CRS first constructs questions and then asks users for their feedback in each conversation session to refine ...

Personalized product search that provides users with customized search services is an important task for e-commerce platforms. This task remains a challenge when inferring users’ preferences from few records or even no records, which is also known as the ...

Aspect-based sentiment classification, which aims at identifying the sentiment polarity of a sentence towards the specified aspect, has become a crucial task for sentiment analysis. Existing methods have proposed effective models and achieved ...

• Exploit the syntactic dependency structure to mine sentence local structure information.
• Construct a word-document graph to explore global word dependency information.
• Propose a novel architecture to encode both global and local ...

In this paper, we aim at the research of rank minimization to find more accurate low-dimensional representations for multi-view subspace learning. The Schatten-p norm is utilized as the rank relaxation function for subspace learning to enhance its ...