A Survey on Session-based Recommender Systems <s> Session-based Recommendations for Multi-Step Recommendations <s> Collaborative filtering (CF) has been widely employed within recommender systems to solve many real-world problems. Learning effective latent factors plays the most important role in collaborative filtering. Traditional CF methods based upon matrix factorization techniques learn the latent factors from the user-item ratings and suffer from the cold start problem as well as the sparsity problem. Some improved CF methods enrich the priors on the latent factors by incorporating side information as regularization. However, the learned latent factors may not be very effective due to the sparse nature of the ratings and the side information. To tackle this problem, we learn effective latent representations via deep learning. Deep learning models have emerged as very appealing in learning effective representations in many applications. In particular, we propose a general deep architecture for CF by integrating matrix factorization with deep feature learning. We provide a natural instantiations of our architecture by combining probabilistic matrix factorization with marginalized denoising stacked auto-encoders. The combined framework leads to a parsimonious fit over the latent features as indicated by its improved performance in comparison to prior state-of-art models over four large datasets for the tasks of movie/book recommendation and response prediction. <s> BIB001 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations for Multi-Step Recommendations <s> We propose an encoder-decoder neural architecture to model user session and intent using browsing and purchasing data from a large e-commerce company. We begin by identifying the source-target transition pairs between items within each session. Then, the set of source items are passed through an encoder, whose learned representation is used by the decoder to estimate the sequence of target items. Therefore, as this process is performed pair-wise, we hypothesize that the model could capture the transition regularities in a more fine grained way. Additionally, our model incorporates an attention mechanism to explicitly learn the more expressive portions of the sequences in order to improve performance. Besides modeling the user sessions, we also extended the original architecture by means of attaching a second decoder that is jointly trained to predict the purchasing intent of user in each session. With this, we want to explore to what extent the model can capture inter session dependencies. We performed an empirical study comparing against several baselines on a large real world dataset, showing that our approach is competitive in both item and intent prediction. <s> BIB002
Siдni f icance. Usually, a shopping event contains multiple steps rather than just one. For example, when a user bought a bread, he may buy milk later, followed by purchasing cheese. Given the partial session consisting of a bread, most current SBRS only make one forward-step recommendations, namely they only make prediction on milk rather than predict milk and then cheese continuously. This may reduce the utility of RS greatly. Open issues. How to generate multi-step recommendations given a partial session as the context? This issue is practical and critical but extremely challenging. Considering the advantage in multi-step modeling, the encoderdecoder framework BIB001 BIB002 may be an intuitive choice to address this issue.