# Auxiliary Information-Enhanced Recommendations
## 2 Related Work
### Sequential Recommendation Algorithms  
Generally speaking, according to the employed techniques, sequential recommendation algorithms can be roughly divided into traditional sequential recommendation algorithms and deep-learning-based sequential recommendation algorithms. Traditional sequential recommendation algorithms are built on traditional data mining or machine learning techniques, including sequential pattern mining, Markov chain models, matrix factorization, and neighborhood models. [PERSON] et al. [5] introduced a personalized sequential pattern mining algorithm to first mine personalized sequential patterns and then utilize the mined patterns for guiding the downstream recommendations. Pattern mining-based algorithms are simple and sometimes effective, but they easily lose those infrequent, but important, items and patterns, and thus reduce the recommendation accuracy. [PERSON] et al. [6] proposed a Markov chain-based SRS algorithm called the Personalized Ranking Metric Embedding (PRME) model for the next POI recommendations. Markov chain-based algorithms can only model the first-order dependencies while ignoring the high-order dependencies, and thus reduce the recommendation accuracy. [PERSON] et al. [7] proposed a classic matrix factorization model called the Factorized Personalized Markov Chains (FPMC) model to factorize the transition matrix over items from adjacent baskets into the latent factors of items. The latent factors are then utilized for next-basket recommendation. However, matrix factorization methods easily suffer from data sparsity issues. In recent years, deep learning models including RNN and CNN have shown great potential to capture the complex relations in sequence, and thus have been widely employed into sequential recommendations. Due to its powerful capability to model sequence data, RNN is the prominent deep model for sequential recommendations. [PERSON] et al. [8] proposed an Gated Recurrent Units (GRU)-equipped RNN-based model called GRU4 Rec for the next-item prediction. GRU4 Rec was further improved by introducing a novel and tailored ranking loss function [9]. Some other similar works include Long Short Term Memory (LSTM)-based SRS algorithms [10]. Later, hierarchical RNN was employed in sequential recommendations to model both intra-sequence dependencies and inter-sequence dependencies for next-item recommendations [11]. However, the rigid order assumption over any two adjacent interactions employed in RNN may lead to generating false sequential dependencies [12]. In addition to RNN, CNN are also applied into sequential recommendations to build CNN-based SRS algorithms. [PERSON] et al. [13] developed a convolutional sequence embedding recommendation model called Caser. Caser employs horizontal and vertical convolutional filters to learn the item-level and feature-level dependencies, respectively, for sequential recommendations. Further, a 3D CNN model was developed for jointly modeling the sequential relations and item content features for next-item recommendations [14]. However, CNN-based SRSs may not be able to effectively capture the long-range dependencies due to the limited perceptive fieldof CNN. Most recently, graph neural networks (GNN), as an advanced deep architecture, have been applied into sequential recommendations. Typical GNN-based sequential recommendation algorithms include memory augmented graph neural networks (MA-GNN) [15] and RetaGNN [16]. Some other researchers employed an attention mechanism into sequential recommendations for improving the recommendation performance. [PERSON] et al. [12] utilized the attention model to learn attentive item and session representations for next-item recommendations. Later, a self-attention mechanism was introduced to better capture those heterogeneous relations embedded in a sequence of interactions for accurate sequential recommendations [17; 18; 19]. Although these deep models have shown great potential in achieving good recommendation performance, they usually ignore the rich auxiliary information.