Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation
Along with the exponential growth of online platforms and services, recommendation systems have become essential for identifying relevant items based on user preferences. The domain of sequential recommendation aims to capture evolving user preferences over time. To address dynamic preference, various contrastive learning methods have been proposed to target data sparsity, a challenge in recommendation systems due to the limited user-item interactions. In this paper, we are the first to apply the Fisher-Merging method to Sequential Recommendation, addressing and resolving practical challenges associated with it. This approach ensures robust fine-tuning by merging the parameters of multiple models, resulting in improved overall performance. Through extensive experiments, we demonstrate the effectiveness of our proposed methods, highlighting their potential to advance the state-of-the-art in sequential learning and recommendation systems.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningRecommendation SystemsSequential RecommendationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Enhancing Sequential Recommendation with Graph Contrastive Learning
The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an…
Auxiliary LearningContrastive LearningRecommendation SystemsSequential RecommendationRelative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair Selection
Contrastive Learning (CL) enhances the training of sequential recommendation (SR) models through informative self-supervision signals. Existing methods often rely on data augmentation strategies to create positive sample…
Contrastive LearningData AugmentationSequential RecommendationCALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation
Traditional recommender systems such as matrix factorization methods have primarily focused on learning a shared dense embedding space to represent both items and user preferences. Subsequently, sequence models such as R…
Language ModelingLanguage ModellingRecommendation SystemsSequential RecommendationContrastive Learning with Bidirectional Transformers for Sequential Recommendation
Contrastive learning with Transformer-based sequence encoder has gained predominance for sequential recommendation. It maximizes the agreements between paired sequence augmentations that share similar semantics. However,…
Contrastive LearningSequential RecommendationHierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation
Sequential recommendation addresses the issue of preference drift by predicting the next item based on the user's previous behaviors. Recently, a promising approach using contrastive learning has emerged, demonstrating i…
Contrastive LearningSequential Recommendation