MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation

By Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, Sehee Chung. Knowledge Discovery and Data Mining, 2019

469 citations recorded by the source corpus.

This paper proposes a recommender system to alleviate the cold-start problem that can estimate user preferences based on only a small number of items. To identify a user's preference in the cold state, existing recommender systems, such as Netflix, initially provide items to a user; we call those items evidence candidates. Recommendations are then made based on the items selected by the user. Previous recommendation studies have two limitations: (1) the users who consumed a few items have poor recommendations and (2) inadequate evidence candidates are used to identify user preferences. We propose a meta-learning-based recommender system called MeLU to overcome these two limitations. From meta-learning, which can rapidly adopt new task with a few examples, MeLU can estimate new user's preferences with a few consumed items. In addition, we provide an evidence candidate selection strategy that determines distinguishing items for customized preference estimation. We validate MeLU with two benchmark datasets, and the proposed model reduces at least 5.92% mean absolute error than two comparative models on the datasets. We also conduct a user study experiment to verify the evidence selection strategy.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/27d2e30bb3ddbed3023340a5ef4d25d4a10bcc70

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Liqiang Nie
5. Yongfeng Zhang
6. Chuan Shi
7. Yin-wei Wei
8. Wenqiang Lei

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Liqiang Nie
5. Yongfeng Zhang
6. Yin-wei Wei
7. Chuan Shi
8. Wenqiang Lei

In Management Science and Operations Research

1. Xiang Wang
2. Xiangnan He
3. Tat-seng Chua
4. Liqiang Nie
5. Wenqiang Lei
6. Chuan Shi
7. Maarten de Rijke
8. Yongfeng Zhang

Citations in the corpus

• Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5) (2022, background): “[27] Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, and Sehee Chung.”
• Advances and Challenges in Conversational Recommender Systems: A Survey (2021, methodology): “In addition, for cold users who have few historical interactions, modeling their preferences from data is difficult [87].”
• Contrastive Learning for Cold-Start Recommendation (2021, background): “Technically, they augment the training data by randomly corrupting warm items’ collaborative embeddings [8, 38, 43] or manually crafting new items [25, 31, 34, 42], so as to enhance the generalization ability to unseen items.”
• Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation (2020, methodology): “[16] applies the MAML framework [9], which rapidly adapts to new users or items based on sparse interaction history.”