SimRec: Mitigating the Cold-Start Problem in Sequential Recommendation by Integrating Item Similarity (2410.22136v1)

Abstract: Sequential recommendation systems often struggle to make predictions or take action when dealing with cold-start items that have limited amount of interactions. In this work, we propose SimRec - a new approach to mitigate the cold-start problem in sequential recommendation systems. SimRec addresses this challenge by leveraging the inherent similarity among items, incorporating item similarities into the training process through a customized loss function. Importantly, this enhancement is attained with identical model architecture and the same amount of trainable parameters, resulting in the same inference time and requiring minimal additional effort. This novel approach results in a robust contextual sequential recommendation model capable of effectively handling rare items, including those that were not explicitly seen during training, thereby enhancing overall recommendation performance. Rigorous evaluations against multiple baselines on diverse datasets showcase SimRec's superiority, particularly in scenarios involving items occurring less than 10 times in the training data. The experiments reveal an impressive improvement, with SimRec achieving up to 78% higher HR@10 compared to SASRec. Notably, SimRec outperforms strong baselines on sparse datasets while delivering on-par performance on dense datasets. Our code is available at https://github.com/amazon-science/sequential-recommendation-using-similarity.

• The paper presents SimRec, a method that integrates item similarity into sequential recommendation frameworks to mitigate the cold-start problem.
• It employs a novel loss function that combines binary cross-entropy with a custom similarity loss derived from text embeddings.
• Experiments on Amazon reviews and benchmark datasets demonstrate up to 78% HR@10 improvement, proving its effectiveness on sparse data.

SimRec: Mitigating the Cold-Start Problem in Sequential Recommendation Systems

The paper "SimRec: Mitigating the Cold-Start Problem in Sequential Recommendation by Integrating Item Similarity," authored by Shaked Brody and Shoval Lagziel, explores an innovative approach to address the cold-start problem in sequential recommendation systems. This issue, prevalent in domains such as e-commerce and content streaming, emerges when systems encounter items with few or no interactions, impeding accurate predictions.

Methodology and Technical Contribution

The core contribution of this research is SimRec, an approach that leverages item similarity to enhance the recommendation process. Unlike many existing methods, SimRec introduces only minimal changes to the model architecture and does not increase inference time, making it highly efficient. The researchers employ a novel loss function, integrating similarity information into the training process without extending learnable parameters.

The methodology encompasses computing item similarities, primarily through text embeddings derived via a pretrained LLM. Cosine similarity from these embeddings helps form a distribution reflecting item similarity across the dataset. Importantly, the proposed loss function, $\mathcal{L}_{SimRec}$, merges binary cross-entropy loss with a custom similarity loss. This latter component compares the model’s prediction distribution against an item's similarity distribution, thereby refining the model's understanding of cold-start items.

Experimental Evaluation

The authors rigorously evaluate SimRec across both sparse and dense datasets. These experiments were performed on Amazon reviews datasets and well-known benchmarks such as ML-1M and Steam. SimRec consistently demonstrates superior performance on datasets characterized by sparsity, notably improving HR@10 by up to 78% over SASRec for items appearing fewer than 10 times in the training set.

A breakdown of performance against item frequency highlights SimRec's strength in predicting cold-start items, where it substantially outweighs SASRec. For datasets where rare items prevail, SimRec's enhancements in recommendation accuracy are evident and significant.

Theoretical and Practical Implications

From a theoretical standpoint, the research substantiates the hypothesis that incorporating item similarity into the learning process can significantly mitigate the cold-start problem. By presenting a method agnostic to specific dataset characteristics or domain-specific knowledge beyond textual features, SimRec sets a precedent for embedding similarity-based approaches into diverse recommendation systems.

Practically, SimRec’s advantage lies in its compatibility with existing architectures and datasets, requiring minimal computational overhead while delivering robust performance improvements. This is particularly beneficial in real-world applications where computational resources and inference latency are critical factors.

Future Directions

The study opens avenues for future exploration. One potential direction could involve assessing the integration of other data modalities beyond text, enhancing the similarity calculation phase with multimodal embeddings. Additionally, further analysis of the scalability of SimRec with expanding dataset sizes could provide insights into its utility in even broader applications.

In conclusion, SimRec represents a significant stride in managing cold-start challenges within sequential recommendation environments. By harmoniously integrating item similarity into the recommendation framework, it offers a promising solution with practical relevance and lays groundwork for continued advancements in this domain.