Enhancing Sequential Recommendation Systems with Diffusion and Attention Models

The field of sequential recommendation systems is witnessing significant advancements, particularly in the areas of generative modeling, diffusion processes, and attention mechanisms. Recent research has focused on enhancing the representation of item embeddings and user preferences through novel diffusion models, which allow for more adaptive and robust predictions by incorporating uncertainty and diverse user interests. These models, such as DiffuRecSys, have demonstrated superior performance in capturing complex user behaviors and preferences, outperforming traditional baselines. Additionally, the integration of attention mechanisms in sequential recommendation, such as in AWRSR, has shown promise in refining attention distributions and improving the learning of high-order dependencies. Furthermore, the introduction of datasets like RecFlow, which include both exposed and unexposed items, is bridging the gap between offline benchmarks and real-world industrial applications, enabling more realistic evaluations and advancements in multi-stage recommendation systems. The field is also seeing innovations in handling cold-start problems and mitigating biases through counterfactual data augmentation and similarity-based approaches, as evidenced by models like SimRec and guided diffusion-based counterfactual augmentation frameworks. Overall, these developments are pushing the boundaries of what sequential recommendation systems can achieve, making them more robust, adaptive, and capable of handling a wider range of real-world scenarios.

Sources

Generative Diffusion Models for Sequential Recommendations

GPRec: Bi-level User Modeling for Deep Recommenders

Beyond Positive History: Re-ranking with List-level Hybrid Feedback

RecFlow: An Industrial Full Flow Recommendation Dataset

Pay Attention to Attention for Sequential Recommendation

Sequential choice in ordered bundles

Guided Diffusion-based Counterfactual Augmentation for Robust Session-based Recommendation

Dual Conditional Diffusion Models for Sequential Recommendation

Modeling Temporal Positive and Negative Excitation for Sequential Recommendation

SimRec: Mitigating the Cold-Start Problem in Sequential Recommendation by Integrating Item Similarity

Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation

A Universal Sets-level Optimization Framework for Next Set Recommendation

Identify Then Recommend: Towards Unsupervised Group Recommendation

Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model

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