# Auxiliary Information-Enhanced Recommendations
## 2 Related Work
### Auxiliary Information-Enhanced Sequential Recommendations  
In the real-world scenarios, in addition to the commonly used item ID information, there is rich auxiliary information related to items, users and interactions. Such auxiliary information can provide more contextual information for an in-depth understanding of users' sequential behaviors, and thus can benefit the subsequent sequential recommendations. For instance, [PERSON] et al. [20] take both the item ID and the corresponding item attributes in a session as the input of a shallow neural networks to learn a compound embedding for each item for the downstream next-item recommendations. A 3D convolutional neural network was proposed by [PERSON] et al. [14] to learn informative item representations from both item IDs and content features of items for next-item prediction. With the introduction of a neighborhood model, [PERSON] et al. [21] incorporated the readily available position information of items within sequences for more accurate sequential recommendations. The occurrenceof timestamps of users' interactions in sequences was explored by [PERSON] et al. [22] and [PERSON] et al. [23] for next-item recommendations. These works have taken a step forward to incorporate more auxiliary information to enhance sequential recommendations, but they ignore the important and representative item image and textual information. Only quite limited works on sequential recommendations have taken item image and/or textual information into account. An RNN-based sequential recommendation model called VCSRS was proposed by [PERSON] et al. [1]. VCSRS first utilizes an attention-based visual feature representation learning component to learn a task-specific item visual representation and then effectively incorporate it into a single LSTM-based RNN to complement the item ID information for next-item recommendations. Although effective, on one hand, VCSRS ignores another important piece of auxiliary information, i.e., item textual information; on the other hand, VCSRS does not model different types of information as different modalities, and thus it fails to effectively capture the sequential dependencies within each type of information, namely each modality, (e.g., item ID and visual feature), respectively, and the interactions between different types of information, namely different modalities. Therefore, VCSRS are different from this work in terms of the input data, the solution and the model architecture. A parallel RNN-based model called p-RNN was developed by [PERSON] et al. [2] to take item IDs, item images and item textual features as the input to learn informative item representations for sequential recommendations. In the parallel RNN model, three RNNs are utilized to model the aforementioned three parts of information, respectively, and the outputs of all RNNs are combined together for the prediction task. Although p-RNN can improve the recommendation performance to some degree, it ignores the interaction relations between different modalities and thus cannot fully model the complex relations embedded in users' interaction sequence data. Another similar work is multi-view RNN (MV-RNN), which also employs both text and image information for sequential recommendations [3]. In MV-RNN, an auto-encoder-based multi-modal representation fusion module is developed to generate a compound representation for each item by integrating both the item image and textual information. The compound representation is then input to a GRU-based RNN for predicting the next item. Although effective, such a method mainly considers the relations across different modalities, while the sequential dependencies within each modality are weakened. In summary, although some works have tried to integrate multi-modal auxiliary information into sequential recommendations, they either ignore the interaction relations across different modalities or weaken the sequential dependencies within modalities. This has limited the further improvement of the recommendation performance.