35 citations · 141 across the 12 of their papers we have counts for
13 papers
Forgetting-aware Linear Bias for Attentive Knowledge Tracing
Yoonjin Im, Eunseong Choi, Heejin Kook +1
Knowledge Tracing (KT) aims to track proficiency based on a question-solving history, allowing us to offer a streamlined curriculum. Recent studies actively utilize attention-based…
Toward a Better Understanding of Loss Functions for Collaborative Filtering
Seongmin Park, Mincheol Yoon, Jae-woong Lee +2
Collaborative filtering (CF) is a pivotal technique in modern recommender systems. The learning process of CF models typically consists of three components: interaction encoder, lo…
It's Enough: Relaxing Diagonal Constraints in Linear Autoencoders for Recommendation
Jaewan Moon, Hye-young Kim, Jongwuk Lee
Linear autoencoder models learn an item-to-item weight matrix via convex optimization with L2 regularization and zero-diagonal constraints. Despite their simplicity, they have show…
uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering
Jae-woong Lee, Seongmin Park, Mincheol Yoon +1
Because implicit user feedback for the collaborative filtering (CF) models is biased toward popular items, CF models tend to yield recommendation lists with popularity bias. Previo…
ConQueR: Contextualized Query Reduction using Search Logs
Hye-young Kim, Minjin Choi, Sunkyung Lee +3
Query reformulation is a key mechanism to alleviate the linguistic chasm of query in ad-hoc retrieval. Among various solutions, query reduction effectively removes extraneous terms…
SpaDE: Improving Sparse Representations using a Dual Document Encoder for First-stage Retrieval
Eunseong Choi, Sunkyung Lee, Minjin Choi +3
Sparse document representations have been widely used to retrieve relevant documents via exact lexical matching. Owing to the pre-computed inverted index, it supports fast ad-hoc s…
Bilateral Self-unbiased Learning from Biased Implicit Feedback
Jae-woong Lee, Seongmin Park, Joonseok Lee +1
Implicit feedback has been widely used to build commercial recommender systems. Because observed feedback represents users' click logs, there is a semantic gap between true relevan…
S-Walk: Accurate and Scalable Session-based Recommendationwith Random Walks
Minjin Choi, Jinhong Kim, Joonsek Lee +2
Session-based recommendation (SR) predicts the next items from a sequence of previous items consumed by an anonymous user. Most existing SR models focus only on modeling intra-sess…
Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentation
Seungho Lee, Minhyun Lee, Jongwuk Lee +1
Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level weak supervision have several limitations: sparse object coverage, inaccurate object boundaries…
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories
Minjin Choi, Sunkyung Lee, Eunseong Choi +4
Automated metaphor detection is a challenging task to identify metaphorical expressions of words in a sentence. To tackle this problem, we adopt pre-trained contextualized models,…
Local Collaborative Autoencoders
Minjin Choi, Yoonki Jeong, Joonseok Lee +1
Top-N recommendation is a challenging problem because complex and sparse user-item interactions should be adequately addressed to achieve high-quality recommendation results. The l…
Session-aware Linear Item-Item Models for Session-based Recommendation
Minjin Choi, jinhong Kim, Joonseok Lee +2
Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e.g., on e-commerce or multimedia streaming services. Spec…
Collaborative Distillation for Top-N Recommendation
Jae-woong Lee, Minjin Choi, Jongwuk Lee +1
Knowledge distillation (KD) is a well-known method to reduce inference latency by compressing a cumbersome teacher model to a small student model. Despite the success of KD in the…