6 papers · 1 filter
GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion
Sunkyung Lee, Minjin Choi, Eunseong Choi +2
Generative recommendation is an emerging paradigm that leverages the extensive knowledge of large language models by formulating recommendations into a text-to-text generation task…
DIFF: Dual Side-Information Filtering and Fusion for Sequential Recommendation
Hye-young Kim, Minjin Choi, Sunkyung Lee +2
Side-information Integrated Sequential Recommendation (SISR) benefits from auxiliary item information to infer hidden user preferences, which is particularly effective for sparse i…
Linear Item-Item Model with Neural Knowledge for Session-based Recommendation
Minjin Choi, Sunkyung Lee, Seongmin Park +1
Session-based recommendation (SBR) aims to predict users' subsequent actions by modeling short-term interactions within sessions. Existing neural models primarily focus on capturin…
Temporal Linear Item-Item Model for Sequential Recommendation
Seongmin Park, Mincheol Yoon, Minjin Choi +1
In sequential recommendation (SR), neural models have been actively explored due to their remarkable performance, but they suffer from inefficiency inherent to their complexity. On…
MARS: Matching Attribute-aware Representations for Text-based Sequential Recommendation
Hyunsoo Kim, Junyoung Kim, Minjin Choi +2
Sequential recommendation aims to predict the next item a user is likely to prefer based on their sequential interaction history. Recently, text-based sequential recommendation has…
Multi-intent-aware Session-based Recommendation
Minjin Choi, Hye-young Kim, Hyunsouk Cho +1
Session-based recommendation (SBR) aims to predict the following item a user will interact with during an ongoing session. Most existing SBR models focus on designing sophisticated…