The amount of content on online music streaming platforms is immense, and most users only access a tiny fraction of this content. Recommender systems are the application of choice to open up the collection to these users. Collaborative filtering has the disadvantage that it relies on explicit ratings, which are often unavailable, and generally disregards the temporal nature of music consumption. On the other hand, item co-occurrence algorithms, such as the recently introduced word2vec-based recommenders, are typically left without an effective user representation. In this paper, we present a new approach to model users through recurrent neural networks by sequentially processing consumed items, represented by any type of embeddings and other context features. This way we obtain semantically rich user representations, which capture a user’s musical taste over time. Our experimental analysis on large-scale user data shows that our model can be used to predict future songs a user will likely listen to, both in the short and long term.
Very recently there have been research efforts in using RNNs for item recommendation. @cite_11 use RNNs to recommend items by predicting the next item interaction. The authors use one-hot item encodings as input and produce scores for every item in the catalog, on which a ranking loss is defined. The task can thus be compared to a classification problem. For millions of items, this quickly leads to scalability issues, and the authors resort to popularity-based sampling schemes to resolve this. Such models typically take a long time to converge, and special care needs to be taken not to introduce a popularity bias, since popular items will occur more frequently in the training data. The work by @cite_23 is closely related to the previous approach, and they also state that making a prediction for each item in the catalog is slow and intractable for many items. Instead, low-dimensional item embeddings can be predicted at the output in a regression task, a notion we will extend on in Section .
@cite_23 @cite_11
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Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task. The models showed promising improvements over traditional recommendation approaches. In this work, we further study RNN-based models for session-based recommendations. We propose the application of two techniques to improve model performance, namely, data augmentation, and a method to account for shifts in the input data distribution. We also empirically study the use of generalised distillation, and a novel alternative model that directly predicts item embeddings. Experiments on the RecSys Challenge 2015 dataset demonstrate relative improvements of 12.8 and 14.8 over previously reported results on the Recall@20 and Mean Reciprocal Rank@20 metrics respectively. We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of long user histories (as in the case of Netflix). In this situation the frequently praised matrix factorization approaches are not accurate. This problem is usually overcome in practice by resorting to item-to-item recommendations, i.e. recommending similar items. We argue that by modeling the whole session, more accurate recommendations can be provided. We therefore propose an RNN-based approach for session-based recommendations. Our approach also considers practical aspects of the task and introduces several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem. Experimental results on two data-sets show marked improvements over widely used approaches.