activity
20222024
most citedForgetting-aware Linear Bias for Attentive Knowledge Tracing

35 citations · 56 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2024

Multi-Granularity Guided Fusion-in-Decoder

Eunseong Choi, Hyeri Lee, Jongwuk Lee

In Open-domain Question Answering (ODQA), it is essential to discern relevant contexts as evidence and avoid spurious ones among retrieved results. The model architecture that uses…

cs.AI202335 cited

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…

cs.IR202313 cited

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…

cs.IR20233 cited

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…

cs.IR20232 cited

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…

cs.IR20232 cited

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…