SimDiffRec: Semantic Similarity-Guided Diffusion for Contrastive Sequential Recommendation
SimDiffRec uses semantic similarity-guided diffusion to enhance sequential recommendation performance, outperforming baselines.
Key Findings
Methodology
SimDiffRec leverages semantic similarity to generate consistent noise and selects augmentation positions based on diffusion model reconstruction confidence. Combined with hard negative sampling, it enhances contrastive learning efficiency and recommendation performance.
Key Results
- On five benchmark datasets, SimDiffRec improved HR@10 and NDCG@10 by 8% and 10% respectively.
- Semantic consistency augmentation significantly reduced semantic information loss compared to random augmentation methods.
- Ablation studies showed that hard negative sampling significantly contributed to performance improvement.
Significance
This study addresses the issue of semantic information loss in random augmentation methods. By using semantic similarity-guided diffusion models, it significantly enhances the robustness and accuracy of sequential recommendation systems.
Technical Contribution
Introduces a semantic similarity-based noise generation method, using diffusion model reconstruction confidence for position selection, offering a new perspective on contrastive learning.
Novelty
First to incorporate semantic similarity into the diffusion model's data augmentation process, significantly improving semantic consistency in sequential recommendations.
Limitations
- Performance improvement is limited in extremely sparse data scenarios.
- High computational resource requirements may limit practical applications.
Future Work
Future work could explore more efficient semantic similarity computation methods and applications on larger-scale datasets.
AI Executive Summary
Sequential recommendation systems play a crucial role in predicting user behavior, but data sparsity remains a major challenge. Existing methods often use random data augmentation, risking semantic information loss. SimDiffRec employs a semantic similarity-guided diffusion model to generate semantically consistent noise, combined with hard negative sampling, significantly enhancing recommendation performance.
Experimental results show that SimDiffRec outperforms existing baseline models across five benchmark datasets, particularly excelling in HR@10 and NDCG@10 metrics. This method not only improves model robustness but also effectively maintains the semantic consistency of user behavior sequences.
Nevertheless, SimDiffRec still has room for improvement in handling extremely sparse data. Future research could focus on more efficient computation methods and applications on larger-scale datasets.
Deep Analysis
Background
Sequential recommendation systems aim to predict future user interests based on past behaviors. Traditional methods like Markov Chains and RNNs are effective for short-term dependencies but struggle with complex patterns and long-term dependencies. Recently, Transformer-based models like SASRec and BERT4Rec have advanced sequence pattern capture, yet data sparsity remains a key challenge.
Core Problem
Existing contrastive learning methods create sequence views through random augmentation, but randomness can disrupt the semantic structure of user behavior, leading to information loss and affecting model discrimination ability.
Innovation
SimDiffRec generates consistent noise through semantic similarity, ensuring semantic consistency in augmented data. It uses diffusion model reconstruction confidence for position selection and combines hard negative sampling to enhance contrastive learning.
Methodology
- �� Generate noise using semantic similarity of item embeddings
- �� Select augmentation positions based on diffusion model reconstruction confidence
- �� Combine with hard negative sampling for contrastive learning to enhance model discrimination
Experiments
Experiments were conducted on Beauty, Toys, Sports, Yelp, and MovieLens datasets, using HR@10 and NDCG@10 as evaluation metrics. SimDiffRec was compared against nine baseline models to validate its effectiveness.
Results
SimDiffRec improved HR@10 and NDCG@10 by 8% and 10% respectively. Ablation studies highlighted the critical role of semantic similarity noise and hard negative sampling in performance enhancement.
Applications
Applicable to recommendation systems requiring high semantic consistency, such as e-commerce product recommendations and streaming service content recommendations.
Limitations & Outlook
Performance improvement is limited in extremely sparse data scenarios. High computational resource requirements may impact practical applications.
Plain Language Accessible to non-experts
Imagine you're in a library trying to find the next book you might like. Traditional methods are like randomly picking books off the shelf, which might miss books you're truly interested in. SimDiffRec is like a smart librarian who recommends books based on your past reading history, considering both the themes you like and the content similarity between books, ensuring the recommendations are what you actually want.
ELI14 Explained like you're 14
Imagine you're playing a game, and the system recommends new games based on the ones you've played before. Traditional methods are like random recommendations, which might not be accurate. SimDiffRec is like a smart game assistant that recommends new games based on the types and styles of games you've played before. It not only looks at the games you've played but also considers the similarity between games, ensuring the recommended games match your taste better.
Glossary
Diffusion Model
A generative model that generates data by gradually adding noise. Used for generating semantically consistent augmented data.
Used to generate semantically consistent augmented data.
Contrastive Learning
A learning method that learns data representations by bringing similar samples closer and pushing dissimilar ones apart. Used to enhance recommendation system discrimination.
Used to enhance recommendation system discrimination.
Semantic Similarity
Measures the degree to which two items are similar in meaning. Used for generating consistent noise.
Used for generating consistent noise.
Hard Negative Sampling
Selects negative samples that are similar but not identical to positive samples, enhancing model discrimination.
Used to enhance contrastive learning.
Reconstruction Confidence
The confidence level of the model's reconstruction result at a position. Used for selecting augmentation positions.
Used for selecting augmentation positions.
Open Questions Unanswered questions from this research
- 1 How to improve model performance in extremely sparse data scenarios? Current methods are limited on sparse data and need more efficient augmentation strategies.
- 2 How to reduce computational resource requirements? The current method's computational complexity is high, limiting large-scale applications.
Applications
Immediate Applications
E-commerce Recommendation
E-commerce platforms can use SimDiffRec to improve product recommendation accuracy and enhance user satisfaction.
Long-term Vision
Intelligent Content Recommendation
In streaming services, SimDiffRec can be used for personalized content recommendations, enhancing user experience.
Abstract
In sequential recommendation systems, data augmentation and contrastive learning techniques have recently been introduced using diffusion models to achieve robust representation learning. However, most of the existing approaches use random augmentation, which risks damaging the contextual information of the original sequence. Accordingly, we propose SimDiffRec: a Semantic Similarity-Guided Diffusion for Contrastive Sequential Recommendation. Our framework leverages the similarity between item embedding vectors to generate semantically consistent noise. Moreover, we utilize high confidence scores in the denoising process to select our augmentation positions. This approach more effectively reflects contextual and structural information compared to augmentation at random positions. From a contrastive learning perspective, the proposed augmentation technique, combined with hard negative sampling, provides more discriminative positive and negative samples, simultaneously improving training efficiency and recommendation performance. Experimental results on five benchmark datasets show that SimDiffRec outperforms the existing baseline models. The code of our framework is available at https://github.com/zingyon/SimDiffRec.