一种用于冷启动和缺失模态场景推荐的多模态单分支嵌入网络
A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios
AI总结：
本文提出多模态单分支嵌入网络SiBraR，通过权重共享编码交互数据与多模态辅助信息，有效解决冷启动与缺失模态场景下的推荐问题，显著优于现有方法。
摘要：
大多数推荐系统采用协同过滤（CF），并基于过去的集体交互提供推荐。因此，当可用交互很少或没有时，CF算法的性能会下降，这种场景被称为冷启动。为了解决这个问题，先前的工作依赖于同时利用协同数据和用户或项目辅助信息的模型。类似于多模态学习，这些模型旨在将协同表示和内容表示组合在一个共享的嵌入空间中。在这项工作中，我们提出了一种用于多模态推荐的新技术，依赖于一种用于推荐的多模态单分支嵌入网络（SiBraR）。利用权重共享，SiBraR使用相同的单分支嵌入网络在不同模态上编码交互数据以及多模态辅助信息。这使得SiBraR在缺失模态场景（包括冷启动）中非常有效。我们在来自三个不同推荐领域（音乐、电影和电子商务）并提供多模态内容信息（音频、文本、图像、标签和交互）的大规模推荐数据集上进行了大量实验，结果表明SiBraR在冷启动场景中显著优于CF以及最先进的基于内容的RSs，并且在热启动场景中具有竞争力。我们表明SiBraR的推荐在缺失模态场景中是准确的，并且该模型能够将不同模态映射到共享嵌入空间的同一区域，从而减小模态差距。
英文摘要：
Most recommender systems adopt collaborative filtering (CF) and provide recommendations based on past collective interactions. Therefore, the performance of CF algorithms degrades when few or no interactions are available, a scenario referred to as cold-start. To address this issue, previous work relies on models leveraging both collaborative data and side information on the users or items. Similar to multimodal learning, these models aim at combining collaborative and content representations in a shared embedding space. In this work we propose a novel technique for multimodal recommendation, relying on a multimodal Single-Branch embedding network for Recommendation (SiBraR). Leveraging weight-sharing, SiBraR encodes interaction data as well as multimodal side information using the same single-branch embedding network on different modalities. This makes SiBraR effective in scenarios of missing modality, including cold start. Our extensive experiments on large-scale recommendation datasets from three different recommendation domains (music, movie, and e-commerce) and providing multimodal content information (audio, text, image, labels, and interactions) show that SiBraR significantly outperforms CF as well as state-of-the-art content-based RSs in cold-start scenarios, and is competitive in warm scenarios. We show that SiBraR's recommendations are accurate in missing modality scenarios, and that the model is able to map different modalities to the same region of the shared embedding space, hence reducing the modality gap.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
