# Auxiliary Information-Enhanced Recommendations
## 4 Experiments
### Performance Comparison with Baselines  
In the experiments, the representative and state-of-the-art sequential recommendation algorithms are selected as the baselines. We compare our proposed algorithm with these baseline algorithms to evaluate the performance of our algorithm. Specifically, the baseline algorithms include two representative sequential recommendation algorithms, BPR [31] and LSTM [32], and four representative and/or state-of-the-art sequential recommendation algorithms which also incorporate auxiliary information such as item images, namely VBPR [33], p-RNN [2], MV-RNN [3] and VCSRS [1]. Within the same setting of this work, p-RNN, MV-RNN and VCSRS incorporate multi-modal auxiliary information, i.e., item images and text, to improve sequential recommendations. VCSRS is adapted to incorporate both item images and text information by concatenating image representation vectors and text representation vectors to form a unified item auxiliary information representation as the input of the feature-level attention module (FAM). All the baseline algorithms and our proposed algorithm are tested on the aforementioned experimental datasets for recommendation performance comparison. Two representative ranking-based measures, recall and mean average precision (MAP), are used as the evaluation metrics. They are commonly used to evaluate the performance of sequential recommendations. The experimental results are shown in Tables 3 and 4, where the values are percentages and the best ones are marked in bold.  
According to Tables 3 and 4, it is clear that our proposed algorithm MFN4 Rec achieved the best performance w.r.t all the evaluation metrics, which demonstrate the effectiveness of our proposed algorithm. BPR and LSTM only take the item ID as the input to model the single-modal sequential dependencies among user-item interactions, and they thus perform the worst. Based on BPR, VBPR adds the item image information as auxiliary information. Based on LSTM, p-RNN adds both the item image and text information for sequential recommendation. The performance improvement of both VBPR and p-RNN is limited; this is because they only model the sequential relations within modalities while ignoring the interaction relations across different modalities. Due to the effective learning of item visual content representation and the careful incorporation of it into the LSTM  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **\# Items** & **\# Users** & **\# Interactions** & **\# Items per User on Average** \\ \hline Amazon Clothing & 38,840 & 22,586 & 272,949 & 12.08 \\ Amazon Phone & 27,879 & 10,429 & 175,645 & 16.84 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The statistics of experimental datasets.  
-based RNN dominated by item ID information, VCSRS can improve the recommendation performance. However, it fails to model the unique sequential dependencies within each modality and the interaction relations between modalities, e.g., item ID and item visual content. The reason is that only one RNN is utilized to model the sequential dependencies over items without a particularly designed component to model the interactions between different modalities. Therefore, the aforementioned sequential dependencies within different modalities are mixed together, and also the interaction relations between different modalities cannot be modelled effectively. Out of all the baseline algorithms, MV-RNN performs the best, demonstrating that the utilization of an auto-encoder can effectively capture the multi-modal information, especially the cross-modal interaction relations. However, it still fails to consider the sequential relations within modalities.  
In comparison, our proposed MFN4 Rec is able to effectively capture the sequential dependencies embedded in multi-modal sequence data by simultaneously capturing both the sequential dependencies within modalities and interaction relations across modalities. As a result, our algorithm can achieve the best performance.