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Found 23 papers, 7 papers with code
Where to Move Next: Zero-shot Generalization of LLMs for Next POI Recommendation
However, the generalization abilities of LLMs still are unexplored to address the next POI recommendations, where users' geographical movement patterns should be extracted.
LIST: Learning to Index Spatio-Textual Data for Embedding based Spatial Keyword Queries
With the proliferation of spatio-textual data, Top-k KNN spatial keyword queries (TkQs), which return a list of objects based on a ranking function that evaluates both spatial and textual relevance, have found many real-life applications.
HPCR: Holistic Proxy-based Contrastive Replay for Online Continual Learning
Inspired by this finding, we propose a novel replay-based method called proxy-based contrastive replay (PCR), which replaces anchor-to-sample pairs with anchor-to-proxy pairs in the contrastive-based loss to alleviate the phenomenon of forgetting.
UER: A Heuristic Bias Addressing Approach for Online Continual Learning
By decomposing the dot-product logits into an angle factor and a norm factor, we empirically find that the bias problem mainly occurs in the angle factor, which can be used to learn novel knowledge as cosine logits.
DRGame: Diversified Recommendation for Multi-category Video Games with Balanced Implicit Preferences
It is centered on multi-category video games, consisting of two {components}: Balance-driven Implicit Preferences Learning for data pre-processing and Clustering-based Diversified Recommendation {Module} for final prediction.
IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation
Specifically, the denoising process considers both social network structure and user interaction interests in a global view.
PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning
It aims to continuously learn new classes from data stream and the samples of data stream are seen only once, which suffers from the catastrophic forgetting issue, i. e., forgetting historical knowledge of old classes.
Gated Mechanism Enhanced Multi-Task Learning for Dialog Routing
Although most existing methods can fulfil this requirement, they can only model single-source dialog data and cannot effectively capture the underlying knowledge of relations among data and subtasks.
Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuning
Recent language generative models are mostly trained on large-scale datasets, while in some real scenarios, the training datasets are often expensive to obtain and would be small-scale.
MSTCGAN: Multiscale time conditional generative adversarial network for long-term satellite image sequence prediction
To address the sequential error accumulation issue, MSTCGAN adopts a parallel prediction framework to produce the future image sequences by a one-hot time condition input.
MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning
Then, resorting to the prior, we split each few-shot task to a set of subtasks with different concept levels and then perform class prediction via a model of decision tree.
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning
Inspired by the recently active and emerging self-supervised learning, in this study, we attempt to enhance the representation power of hyperbolic graph models by drawing upon the advantages of contrastive learning.
SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification
In this framework, a scene graph construction module is carefully designed to represent each test remote sensing image or each scene class as a scene graph, where the nodes reflect these co-occurrence objects meanwhile the edges capture the spatial correlations between these co-occurrence objects.
Prototype Completion for Few-Shot Learning
In this paper, 1) we figure out the reason, i. e., in the pre-trained feature space, the base classes already form compact clusters while novel classes spread as groups with large variances, which implies that fine-tuning feature extractor is less meaningful; 2) instead of fine-tuning feature extractor, we focus on estimating more representative prototypes.
Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation
Hence, the key is to make full use of rich interaction information among streamers, users, and products.
Exploiting Global Contextual Information for Document-level Named Entity Recognition
To this end, we propose a model called Global Context enhanced Document-level NER (GCDoc) to leverage global contextual information from two levels, i. e., both word and sentence.
MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning
Although the existing meta-optimizers can also be adapted to our framework, they all overlook a crucial gradient bias issue, \emph{i. e.}, the mean-based gradient estimation is also biased on sparse data.
Exploiting Group-level Behavior Pattern forSession-based Recommendation
In RNMSR, we propose to learn the user preference from both instance-level and group-level, respectively: (i) instance-level, which employs GNNs on a similarity-based item-pairwise session graph to capture the users' preference in instance-level.
Exploring Global Information for Session-based Recommendation
Based on BGNN, we propose a novel approach, called Session-based Recommendation with Global Information (SRGI), which infers the user preferences via fully exploring global item-transitions over all sessions from two different perspectives: (i) Fusion-based Model (SRGI-FM), which recursively incorporates the neighbor embeddings of each node on global graph into the learning process of session level item representation; and (ii) Constrained-based Model (SRGI-CM), which treats the global-level item-transition information as a constraint to ensure the learned item embeddings are consistent with the global item-transition.
User-based Network Embedding for Collective Opinion Spammer Detection
Due to the huge commercial interests behind online reviews, a tremendousamount of spammers manufacture spam reviews for product reputation manipulation.
Target Guided Emotion Aware Chat Machine
The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions.
A Survey on Recent Advances in Sequence Labeling from Deep Learning Models
Sequence labeling (SL) is a fundamental research problem encompassing a variety of tasks, e. g., part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc.
A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC
Recent years have witnessed the proliferation of Massive Open Online Courses (MOOCs).
