Context-aware recommendation systems improve upon classical recommender systems by including, in the modelling, a user's behaviour. Research into context-aware recommendation systems has previously only considered the sequential ordering of items as contextual information. However, there is a wealth of unexploited additional multi-modal information available in auxiliary knowledge related to items. This study extends the existing research by evaluating a multi-modal recommendation system that exploits the inclusion of comprehensive auxiliary knowledge related to an item. The empirical results explore extracting vector representations (embeddings) from unstructured and structured data using data2vec. The fused embeddings are then used to train several state-of-the-art transformer architectures for sequential user-item representations. The analysis of the experimental results shows a statistically significant improvement in prediction accuracy, which confirms the effectiveness of including auxiliary information in a context-aware recommendation system. We report a 4% and 11% increase in the NDCG score for long and short user sequence datasets, respectively.
翻译:通过在模型中包括用户的行为,改进了传统推荐系统的背景意识建议系统。对背景意识建议系统的研究以前只将项目顺序顺序视为背景信息。然而,在辅助知识中,与项目有关的辅助知识中有大量未开发的额外多模式信息。本研究通过评价利用包含与项目有关的全面辅助知识的多模式建议系统,扩展了现有研究。实验结果探索了利用数据2vec从未结构化和结构化的数据中提取矢量表示(组合)的方法。接合嵌入器随后用于为连续用户项目展示培训几个最先进的变异器结构。实验结果分析表明,预测准确性在统计上有了显著的提高,这证实了将辅助信息纳入背景建议系统的有效性。我们报告,在长、短用户序列数据集中,NDCG的评分分别增加了4%和11%。