Building an effective sequential recommendation system is still a challenging task due to limited interactions among users and items. Recent work has shown the effectiveness of incorporating textual or visual information into sequential recommendation to alleviate the data sparse problem. The data sparse problem now is attracting a lot of attention in both industry and academic community. However, considering interactions among modalities on a sequential scenario is an interesting yet challenging task because of multimodal heterogeneity. In this paper, we introduce a novel recommendation approach of considering both textual and visual information, namely Multimodal Interactive Network (MIN). The advantage of MIN lies in designing a learning framework to leverage the interactions among modalities from both the item level and the sequence level for building an efficient system. Firstly, an item-wise interactive layer based on the encoder-decoder mechanism is utilized to model the item-level interactions among modalities to select the informative information. Secondly, a sequence interactive layer based on the attention strategy is designed to capture the sequence-level preference of each modality. MIN seamlessly incorporates interactions among modalities from both the item level and the sequence level for sequential recommendation. It is the first time that interactions in each modality have been explicitly discussed and utilized in sequential recommenders. Experimental results on four real-world datasets show that our approach can significantly outperform all the baselines in sequential recommendation task.

Next basket prediction attempts to provide sequential recommendations to users based on a sequence of the user’s previous purchases. Ideally, a good prediction model should be able to explore the personalized preference of the users, as well as the sequential relations of the items. This goal of modeling becomes even more challenging when both factors are time-dependent. However, existing methods either take these two aspects as static, or only consider temporal dynamics for one of the two aspects. In this work, we propose the dynamic representation learning approach for time-dependent next basket recommendation, which jointly models the dynamic nature of user preferences and item relations. To do so, we explicitly model the transaction timestamps, as well as the dynamic representations of both users and items, so as to capture the personalized user preference on each individual item dynamically. Experiments on three real-world retail datasets show that our method significantly outperforms several state-of-the-art methods for next basket recommendation.