An explainable sequence-based deep learning predictor with applications to song recommendation and text classification.

Streaming applications are now the predominant tools for listening to music. What makes the success of such software is the availability of songs and especially their ability to provide users with relevant personalized recommendations. State of the art music recommender systems mainly rely on either Matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction (listening to a song) using a memory-based deep learning structure that learns from temporal sequences of user actions. Despite advances in deep learning models for song recommendation systems, none has taken advantage of the sequential nature of songs by learning sequence models that are based on content. Aside from the importance of prediction accuracy in recommendation systems, recent research has unveiled the importance of other significant aspects such as explainability and solving the cold start problem where a new user or item with no prior history of interactions joins an online platform. In this work, we propose a hybrid deep learning structure, called “SeER”, that uses collaborative filtering and deep sequence models on the MIDI content of songs for recommendation. Our approach aims to take advantage of the superior capabilities of re-current neural networks, the multidimensional time series aspect of songs, and the power of matrix factorization to: •provide more accurate personalized recommendations, •solve the item cold start problem which is in the case of where a new unrated song is added to the set of choices to recommend; and •generate a relevant explanation for a song recommendation using a novel explainability process we named “Segment Forward Propagation Explainability”. Our evaluation experiments show promising results compared to state of the art baseline and hybrid song recommender systems in terms of ranking evaluation. In addition, we demonstrate how our explanation mechanism can be used with generic sequential data beyond music, namely unstructured free text in two application domains: sentiment classification of online user reviews and delineating potential child abuse instances from medical examination reports

Attentive Aspect Modeling for Review-aware Recommendation

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product. However, these aspect-based methods suffer from two problems. First, the common aspects are usually very sparse, which is caused by the sparsity of user-product interactions and the diversity of individual users' vocabularies. Second, a user's interests on aspects could be different with respect to different products, which are usually assumed to be static in existing methods. In this paper, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges. For the first problem, to enrich the aspect connections between user and product, besides common aspects, AARM also models the interactions between synonymous and similar aspects. For the second problem, a neural attention network which simultaneously considers user, product and aspect information is constructed to capture a user's attention towards aspects when examining different products. Extensive quantitative and qualitative experiments show that AARM can effectively alleviate the two aforementioned problems and significantly outperforms several state-of-the-art recommendation methods on top-N recommendation task.Comment: Camera-ready manuscript for TOI