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Rich user behavior data has been proven to be of great value for recommendation systems. Modeling lifelong user behavior data in the retrieval stage to explore user long-term preference and obtain comprehensive retrieval results is crucial. Existing ...

Multi-view Graph Learning is popular in recommendations due to its ability to capture relationships and connections across multiple views. Existing multi-view graph learning methods generally involve constructing graphs of views and performing ...

The item cold-start problem is crucial for online recommender systems, as the success of the cold-start phase determines whether items can transition into popular ones. Prompt learning, a powerful technique used in natural language processing (NLP) to ...

Short video has witnessed rapid growth in the past few years in multimedia platforms. To ensure the freshness of the videos, platforms receive a large number of user-uploaded videos every day, making collaborative filtering-based recommender methods ...

Low-resource Complex Named Entity Recognition aims to detect entities with the form of any linguistic constituent under scenarios with limited manually annotated data. Existing studies augment the text through the substitution of same type entities or ...

Next point-of-interest (POI) recommendation has been a prominent and trending task to provide next suitable POI suggestions for users. Most existing sequential-based and graph neural network-based methods have explored various approaches to modeling user ...

Next point-of-interest (POI) recommendation is of great importance for both location-based service providers and users. Current state-of-the-art methods view users and POIs as unified latent representations, and model users’ transition patterns ...

The event detection (ED) task aims to extract structured event information from unstructured text. Recent works in ED rely heavily on annotated training data and often lack the ability to construct semantic knowledge, leading to a significant ...

Next point-of-interest (POI) recommendation has been a prominent and trending task to provide next suitable POI suggestions for users. Current state-of-the-art studies have achieved considerable performances by modeling user-POI interactions or ...

Cross-domain recommendation (CDR) aims to alleviate the data sparsity by transferring knowledge from an informative source domain to the target domain, which inevitably proposes stern challenges to data privacy and transferability during the transfer ...