24,446 research outputs found
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
RNNs have been shown to be excellent models for sequential data and in
particular for data that is generated by users in an session-based manner. The
use of RNNs provides impressive performance benefits over classical methods in
session-based recommendations. In this work we introduce novel ranking loss
functions tailored to RNNs in the recommendation setting. The improved
performance of these losses over alternatives, along with further tricks and
refinements described in this work, allow for an overall improvement of up to
35% in terms of MRR and Recall@20 over previous session-based RNN solutions and
up to 53% over classical collaborative filtering approaches. Unlike data
augmentation-based improvements, our method does not increase training times
significantly. We further demonstrate the performance gain of the RNN over
baselines in an online A/B test.Comment: CIKM'18, authors' versio
Recurrent Latent Variable Networks for Session-Based Recommendation
In this work, we attempt to ameliorate the impact of data sparsity in the
context of session-based recommendation. Specifically, we seek to devise a
machine learning mechanism capable of extracting subtle and complex underlying
temporal dynamics in the observed session data, so as to inform the
recommendation algorithm. To this end, we improve upon systems that utilize
deep learning techniques with recurrently connected units; we do so by adopting
concepts from the field of Bayesian statistics, namely variational inference.
Our proposed approach consists in treating the network recurrent units as
stochastic latent variables with a prior distribution imposed over them. On
this basis, we proceed to infer corresponding posteriors; these can be used for
prediction and recommendation generation, in a way that accounts for the
uncertainty in the available sparse training data. To allow for our approach to
easily scale to large real-world datasets, we perform inference under an
approximate amortized variational inference (AVI) setup, whereby the learned
posteriors are parameterized via (conventional) neural networks. We perform an
extensive experimental evaluation of our approach using challenging benchmark
datasets, and illustrate its superiority over existing state-of-the-art
techniques
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