# Auxiliary Information-Enhanced Recommendations
###### Abstract  
Sequential recommendations have attracted increasing attention from both academia and industry in recent years. They predict a given user's next choice of items by mainly modeling the sequential relations over a sequence of the user's interactions with the items. However, most of the existing sequential recommendation algorithms mainly focus on the sequential dependencies between item IDs within sequences, while ignoring the rich and complex relations embedded in the auxiliary information, such as items' image information and textual information. Such complex relations can help us better understand users' preferences towards items, and thus benefit from the recommendations. To bridge this gap, we propose an auxiliary information-enhanced sequential recommendation algorithm called memory fusion network for recommendation (MFN4 Rec) to incorporate both items' image and textual information for sequential recommendations. Accordingly, item IDs, item image information and item textual information are regarded as three modalities. By comprehensively modelling the sequential relations within modalities and interaction relations across modalities, MFN4 Rec can learn a more informative representation of users' preferences for more accurate recommendations. Extensive experiments on two real-world datasets demonstrate the superiority of MFN4 Rec over state-of-the-art sequential recommendation algorithms. recommendations; recommendation; recommender systems; auxiliary information +
Footnote †: 2021: 23 September 2021  
In the sequential recommendation scenarios, there are more complex relations embedded in the aforementioned multi-modal information. To be specific, there are not only _sequential relations within modalities_, e.g., a user's implicit interactions (clicks) with items usually being sequentially dependent, but also _interaction relations between different modalities_, e.g., the correlations between the item description texts and item appearance images. However, most of the existing SRS algorithms either ignore such auxiliary multi-model information or simply model a single type of relation embedded within such auxiliary information. For example, the visual content-enhanced sequential recommender system (VCSRS) first learns an attentive item visual content representation and then incorporates it into an LSTM-based recurrent neural network (RNN) for next-item recommendations [1]. However, VCSRS not only ignores the richer textual description information of items (e.g., reviews), but also fails to model the sequential dependencies within each modality as well as the interaction relations across different modalities. The parallel recurrent neural network (p-RNN) first utilizes multi recurrent neural networks to model the sequential dependencies over items embedded in user-item interactions, i.e., clicks, item description texts and item images, separately and then integrate the modeled sequential dependencies from different modalities together for the downstream recommendations [2]. However, p-RNN only models the sequential dependencies within each modality while ignoring the complex interaction relations across different modalities. Multi-view RNN (MV-RNN) also employs both text and image information for sequential recommendations [3]. In MV-RNN, an auto-encoder-based multi-modal representation fusion module is designed to generate a compound representation for each item by integrating the item-related information from multiple modalities. The compound representation of a given item is then input into a gated recurrent unit (GRU) of the corresponding time step of an RNN to model the sequential dependencies among items. Finally, the final hidden state of the RNN is regarded as the user's preference for generating recommendations. Although effective, such a method mainly considers the interaction relations across different modalities, while the sequential dependencies within each modality are weakened.