In sparse recommender settings, users' context and item attributes play a crucial role in deciding which items to recommend next. Despite that, recent works in sequential and time-aware recommendations usually either ignore both aspects or only consider one of them, limiting their predictive performance. In this paper, we address these limitations by proposing a context and attribute-aware recommender model (CARCA) that can capture the dynamic nature of the user profiles in terms of contextual features and item attributes via dedicated multi-head self-attention blocks that extract profile-level features and predicting item scores. Also, unlike many of the current state-of-the-art sequential item recommendation approaches that use a simple dot-product between the most recent item's latent features and the target items embeddings for scoring, CARCA uses cross-attention between all profile items and the target items to predict their final scores. This cross-attention allows CARCA to harness the correlation between old and recent items in the user profile and their influence on deciding which item to recommend next. Experiments on four real-world recommender system datasets show that the proposed model significantly outperforms all state-of-the-art models in the task of item recommendation and achieving improvements of up to 53% in Normalized Discounted Cumulative Gain (NDCG) and Hit-Ratio. Results also show that CARCA outperformed several state-of-the-art dedicated image-based recommender systems by merely utilizing image attributes extracted from a pre-trained ResNet50 in a black-box fashion.
翻译:在稀少的推荐人设置中,用户的上下文和项目属性在决定下一个项目时发挥着关键作用。尽管最近有关顺序和时间意识建议的工作通常忽视这两个方面,或者只考虑其中的一个方面,从而限制其预测性性能。在本文件中,我们通过提出一个背景和属性认知建议模型(CARCA)来解决这些局限性,该模型能够通过专门的多头自我关注区块,提取配置级别特性并预测项目分数,从背景特征和项目属性的角度反映用户概况的动态性质。此外,与目前许多最先进的顺序项目建议采用的方法不同,即使用最新项目潜在特征与嵌入评分的目标项目之间的简单点产品,从而限制其预测性能。 CARCA使用所有配置项目和目标项目之间的交叉注意模型来预测其最后分数。这种交叉关注使CARCA能够利用用户配置中旧项目和最新项目之间的关联,以及它们对决定下一步项目建议的影响。在四个真实世界推荐的系统设置上进行实验,显示拟议模型大大超越了最新版本的图像属性,在SAL-SAL-S-SAFAFAFAA的S-S-SAFADADAFAFAFSAAAAASASADSASASASASADSDSDSASASASAD 中,该SADSASAASASASASASASASASASAAAAAASDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDS 中,该模型中,该模型中,该模型中,该SDADADSDADADADADADADADADADADADADADADADADADADADADADADADADADADADASASDASASASA