MeLU

Lee, Hoyeop; Im, Jinbae; Jang, Seongwon; Cho, Hyunsouk; Chung, Sehee

doi:10.1145/3292500.3330859

Cited by 256 publications

(39 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some works use meta-learning to address cold-start recommendation. For example, MeLU [15] uses two sets, namely, the support set and query set. The support set and query set are used for calculating training loss and test loss for each recommendation task.…”

Section: Related Work 51 Cold-start Recommendationmentioning

confidence: 99%

Contrastive Collaborative Filtering for Cold-Start Item Recommendation

Zhou,

Zhang,

Yang

2023

Preprint

View full text Add to dashboard Cite

The cold-start problem is a long-standing challenge in recommender systems. As a promising solution, content-based generative models usually project a cold-start item's content onto a warm-start item embedding to capture collaborative signals from item content so that collaborative filtering can be applied. However, since the training of the cold-start recommendation models is conducted on warm datasets, the existent methods face the issue that the collaborative embeddings of items will be blurred, which significantly degenerates the performance of cold-start item recommendation. To address this issue, we propose a novel model called Contrastive Collaborative Filtering for Cold-start item Recommendation (CCFCRec), which capitalizes on the co-occurrence collaborative signals in warm training data to alleviate the issue of blurry collaborative embeddings for cold-start item recommendation. In particular, we devise a contrastive collaborative filtering (CF) framework, consisting of a content CF module and a co-occurrence CF module to generate the content-based collaborative embedding and the co-occurrence collaborative embedding for a training item, respectively. During the joint training of the two CF modules, we apply a contrastive learning between the two collaborative embeddings, by which the knowledge about the co-occurrence signals can be indirectly transferred to the content CF module, so that the blurry collaborative embeddings can be rectified implicitly by the memorized co-occurrence collaborative signals during the applying phase. Together with the sound theoretical analysis, the extensive experiments conducted on real datasets demonstrate the superiority of the proposed model. The codes and datasets are available on https://github.com/zzhin/CCFCRec.

show abstract

Section: Related Work 51 Cold-start Recommendationmentioning

confidence: 99%

Contrastive Collaborative Filtering for Cold-Start Item Recommendation

Zhou,

Zhang,

Yang

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In this work, we propose a novel attributeselection and a preference fusion strategy that jointly address these two issues. Note that while there are other research directions in CRSs including dialogue understanding [10,14,28,37,40], response generation [16,18,20,39], and exploration-exploitation trade-offs [7,11,31,32,38] those are not the focus of this work.…”

Section: Related Workmentioning

confidence: 99%

Enhancing User Personalization in Conversational Recommenders

Lin¹,

Zhu²,

Wang³

et al. 2023

Preprint

View full text Add to dashboard Cite

Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience. Through a back-and-forth dialogue, users can quickly hone in on just the right items. Many approaches to conversational recommendation, however, only partially explore the user preference space and make limiting assumptions about how user feedback can be best incorporated, resulting in long dialogues and poor recommendation performance. In this paper, we propose a novel conversational recommendation framework with two unique features: (i) a greedy NDCG attribute selector, to enhance user personalization in the interactive preference elicitation process by prioritizing attributes that most effectively represent the actual preference space of the user; and (ii) a user representation refiner, to effectively fuse together the user preferences collected from the interactive elicitation process to obtain a more personalized understanding of the user. Through extensive experiments on four frequently used datasets, we find the proposed framework not only outperforms all the state-of-the-art conversational recommenders (in terms of both recommendation performance and conversation efficiency), but also provides a more personalized experience for the user under the proposed multigroundtruth multi-round conversational recommendation setting. CCS CONCEPTS• Information systems → Recommender systems.

show abstract

“…The basic idea behind meta-learning in recommendation systems is to formulate the user cold-start recommendation as a few-shot learning problem and train the model to adapt to new (i.e., unseen) users rapidly. MeLU [17] integrates user and item attributes with the MAML algorithm, and MAMO [4] further uses task-specific and feature-specific memories to keep individual users' preferences. MetaCSR [12] incorporates the gradientbased meta-learner and the diffusion representer that is composed of graph convolutional networks (GCNs) [16] to model high-order user-item correlation without side information (e.g., age, job).…”

Section: Introductionmentioning

confidence: 99%

Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation

Kim¹,

Yang²,

Ryu³

et al. 2023

Preprint

View full text Add to dashboard Cite

Sequential recommenders have made great strides in capturing a user's preferences. Nevertheless, the cold-start recommendation remains a fundamental challenge in which only a few user-item interactions are available for personalization. Gradient-based metalearning approaches have recently emerged in the sequential recommendation field due to their fast adaptation and easy-to-integrate abilities. The meta-learning algorithms formulate the cold-start recommendation as a few-shot learning problem, where each user is represented as a task to be adapted. However, while meta-learning algorithms generally assume that task-wise samples are evenly distributed over classes or values, user-item interactions are not that way in real-world applications (e.g., watching favorite videos multiple times, leaving only good ratings and no bad ones). As a result, in the real-world, imbalanced user feedback that accounts for most task training data may dominate the user adaptation and prevent meta-learning algorithms from learning meaningful metaknowledge for personalized recommendations. To alleviate this limitation, we propose a novel sequential recommendation framework based on gradient-based meta-learning that captures the imbalance of each user's rating distribution and accordingly computes adaptive loss for user-specific learning. It is the first work to tackle the impact of imbalanced ratings in cold-start sequential recommendation scenarios. We design adaptive weighted loss and improve the existing meta-learning algorithms for state-of-the-art sequential recommendation methods. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of our framework.

show abstract

MeLU

Cited by 256 publications

References 21 publications

Contrastive Collaborative Filtering for Cold-Start Item Recommendation

Contrastive Collaborative Filtering for Cold-Start Item Recommendation

Enhancing User Personalization in Conversational Recommenders

Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation

Contact Info

Product

Resources

About