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Found 51 papers, 27 papers with code
Multi-Domain Recommendation to Attract Users via Domain Preference Modeling
Targeting a platform that operates multiple service domains, we introduce a new task, Multi-Domain Recommendation to Attract Users (MDRAU), which recommends items from multiple ``unseen'' domains with which each user has not interacted yet, by using knowledge from the user's ``seen'' domains.
Exploring Language Model's Code Generation Ability with Auxiliary Functions
However, our analysis also reveals the model's underutilized behavior to call the auxiliary function, suggesting the future direction to enhance their implementation by eliciting the auxiliary function call ability encoded in the models.
Rectifying Demonstration Shortcut in In-Context Learning
However, LLMs often rely on their pre-trained semantic priors of demonstrations rather than on the input-label relationships to proceed with ICL prediction.
Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy
Document retrieval has greatly benefited from the advancements of large-scale pre-trained language models (PLMs).
Improving Matrix Completion by Exploiting Rating Ordinality in Graph Neural Networks
We introduce a new method, called ROGMC, to leverage Rating Ordinality in GNN-based Matrix Completion.
KoDialogBench: Evaluating Conversational Understanding of Language Models with Korean Dialogue Benchmark
As language models are often deployed as chatbot assistants, it becomes a virtue for models to engage in conversations in a user's first language.
Doubly Calibrated Estimator for Recommendation on Data Missing Not At Random
On this basis, we propose a Doubly Calibrated Estimator that involves the calibration of both the imputation and propensity models.
Top-Personalized-K Recommendation
To address this issue, we introduce Top-Personalized-K Recommendation, a new recommendation task aimed at generating a personalized-sized ranking list to maximize individual user satisfaction.
Deep Rating Elicitation for New Users in Collaborative Filtering
Recent recommender systems started to use rating elicitation, which asks new users to rate a small seed itemset for inferring their preferences, to improve the quality of initial recommendations.
Distillation from Heterogeneous Models for Top-K Recommendation
Our work aims to transfer the ensemble knowledge of heterogeneous teachers to a lightweight student model using knowledge distillation (KD), to reduce the huge inference costs while retaining high accuracy.
Learning Topology-Specific Experts for Molecular Property Prediction
In this paper, motivated by this observation, we propose TopExpert to leverage topology-specific prediction models (referred to as experts), each of which is responsible for each molecular group sharing similar topological semantics.
Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation
To this end, we first address the characteristics of multi-behavior sequences that should be considered in SRSs, and then propose novel methods for Dynamic Multi-behavior Sequence modeling named DyMuS, which is a light version, and DyMuS+, which is an improved version, considering the characteristics.
Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization
We formulate the unsupervised summarization based on the Markov decision process with rewards representing the summary quality.
Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation
Topic taxonomies display hierarchical topic structures of a text corpus and provide topical knowledge to enhance various NLP applications.
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability
We first formulate a task that requires to predict which items each user will consume in the recent period of the training time based on users' consumption history.
Ranked #1 on
Sequential Recommendation
on Amazon Cell Phones
Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering
ConCF constructs a multi-branch variant of a given target model by adding auxiliary heads, each of which is trained with heterogeneous objectives.
Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning
In the end, we propose CLRCMD, a contrastive learning framework that optimizes RCMD of sentence pairs, which enhances the quality of sentence similarity and their interpretation.
TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters
Topic taxonomies, which represent the latent topic (or category) structure of document collections, provide valuable knowledge of contents in many applications such as web search and information filtering.
Obtaining Calibrated Probabilities with Personalized Ranking Models
Extensive evaluations with various personalized ranking models on real-world datasets show that both the proposed calibration methods and the unbiased empirical risk minimization significantly improve the calibration performance.
Out-of-Category Document Identification Using Target-Category Names as Weak Supervision
To address this challenge, we introduce a new task referred to as out-of-category detection, which aims to distinguish the documents according to their semantic relevance to the inlier (or target) categories by using the category names as weak supervision.
Learnable Structural Semantic Readout for Graph Classification
By the help of a global readout operation that simply aggregates all node (or node-cluster) representations, existing GNN classifiers obtain a graph-level representation of an input graph and predict its class label using the representation.
Weakly Supervised Temporal Anomaly Segmentation with Dynamic Time Warping
Most recent studies on detecting and localizing temporal anomalies have mainly employed deep neural networks to learn the normal patterns of temporal data in an unsupervised manner.
OoMMix: Out-of-manifold Regularization in Contextual Embedding Space for Text Classification
Recent studies on neural networks with pre-trained weights (i. e., BERT) have mainly focused on a low-dimensional subspace, where the embedding vectors computed from input words (or their contexts) are located.
Unsupervised Proxy Selection for Session-based Recommender Systems
Session-based Recommender Systems (SRSs) have been actively developed to recommend the next item of an anonymous short item sequence (i. e., session).
Topology Distillation for Recommender System
To address this issue, we propose a novel method named Hierarchical Topology Distillation (HTD) which distills the topology hierarchically to cope with the large capacity gap.
Bidirectional Distillation for Top-K Recommender System
Recommender systems (RS) have started to employ knowledge distillation, which is a model compression technique training a compact model (student) with the knowledge transferred from a cumbersome model (teacher).
Out-of-Manifold Regularization in Contextual Embedding Space for Text Classification
Recent studies on neural networks with pre-trained weights (i. e., BERT) have mainly focused on a low-dimensional subspace, where the embedding vectors computed from input words (or their contexts) are located.
Bootstrapping User and Item Representations for One-Class Collaborative Filtering
To make the representations of positively-related users and items similar to each other while avoiding a collapsed solution, BUIR adopts two distinct encoder networks that learn from each other; the first encoder is trained to predict the output of the second encoder as its target, while the second encoder provides the consistent targets by slowly approximating the first encoder.
Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation
Existing studies regard the time information as a single type of feature and focus on how to associate it with user preferences on items.
Learnable Dynamic Temporal Pooling for Time Series Classification
With the increase of available time series data, predicting their class labels has been one of the most important challenges in a wide range of disciplines.
Multi-Class Data Description for Out-of-distribution Detection
The capability of reliably detecting out-of-distribution samples is one of the key factors in deploying a good classifier, as the test distribution always does not match with the training distribution in most real-world applications.
One-class Classification Robust to Geometric Transformation
Recent studies on one-class classification have achieved a remarkable performance, by employing the self-supervised classifier that predicts the geometric transformation applied to in-class images.
DE-RRD: A Knowledge Distillation Framework for Recommender System
Recent recommender systems have started to employ knowledge distillation, which is a model compression technique distilling knowledge from a cumbersome model (teacher) to a compact model (student), to reduce inference latency while maintaining performance.
Building Large-Scale English and Korean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain
We release large-scale datasets of users{'} comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain.
Interest Sustainability-Aware Recommender System
More precisely, we first predict the interest sustainability of each item, that is, how likely each item will be consumed in the future.
Sparse Network Inversion for Key Instance Detection in Multiple Instance Learning
Since a positive bag contains both positive and negative instances, it is often required to detect positive instances (key instances) when a set of instances is categorized as a positive bag.
Unsupervised Differentiable Multi-aspect Network Embedding
To capture the multiple aspects of each node, existing studies mainly rely on offline graph clustering performed prior to the actual embedding, which results in the cluster membership of each node (i. e., node aspect distribution) fixed throughout training of the embedding model.
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network
We view the heterogeneous network embedding as simultaneously solving multiple tasks in which each task corresponds to each relation type in a network.
Unsupervised Attributed Multiplex Network Embedding
Even for those that consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and fail to model the global properties of a graph.
Deep Generative Classifier for Out-of-distribution Sample Detection
The capability of reliably detecting out-of-distribution samples is one of the key factors in deploying a good classifier, as the test distribution always does not match with the training distribution in most real-world applications.
Step Size Optimization
This paper proposes a new approach for step size adaptation in gradient methods.
Out-of-Distribution Image Detection Using the Normalized Compression Distance
Inspired by the method using the global average pooling on the feature maps of the convolutional neural networks, the goal of our method is to extract informative sequential patterns from the feature maps.
Task-Guided Pair Embedding in Heterogeneous Network
In this paper, we propose a novel task-guided pair embedding framework in heterogeneous network, called TaPEm, that directly models the relationship between a pair of nodes that are related to a specific task (e. g., paper-author relationship in author identification).
Collaborative Translational Metric Learning
We also conduct extensive qualitative evaluations on the translation vectors learned by our proposed method to ascertain the benefit of adopting the translation mechanism for implicit feedback-based recommendations.
Ranked #1 on
Recommendation Systems
on Declicious
Out-of-domain Detection based on Generative Adversarial Network
The main goal of this paper is to develop out-of-domain (OOD) detection for dialog systems.
Generative Adversarial Network
Out of Distribution (OOD) Detection
+1
Neural Sentence Embedding using Only In-domain Sentences for Out-of-domain Sentence Detection in Dialog Systems
Then we used domain-category analysis as an auxiliary task to train neural sentence embedding for OOD sentence detection.
Click-aware purchase prediction with push at the top
We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but-not-purchased, and non-clicked items, to study the usefulness of leveraging click records.
Federated Tensor Factorization for Computational Phenotyping
In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data.
Automatic Open Knowledge Acquisition via Long Short-Term Memory Networks with Feedback Negative Sampling
To the best of our knowledge, this is the first work to apply deep learning to Open IE.
Analyzing Pre-processing Settings for Urdu Single-document Extractive Summarization
Preprocessing is a preliminary step in many fields including IR and NLP.
