587 research outputs found
Towards Neural Mixture Recommender for Long Range Dependent User Sequences
Understanding temporal dynamics has proved to be highly valuable for accurate
recommendation. Sequential recommenders have been successful in modeling the
dynamics of users and items over time. However, while different model
architectures excel at capturing various temporal ranges or dynamics, distinct
application contexts require adapting to diverse behaviors. In this paper we
examine how to build a model that can make use of different temporal ranges and
dynamics depending on the request context. We begin with the analysis of an
anonymized Youtube dataset comprising millions of user sequences. We quantify
the degree of long-range dependence in these sequences and demonstrate that
both short-term and long-term dependent behavioral patterns co-exist. We then
propose a neural Multi-temporal-range Mixture Model (M3) as a tailored solution
to deal with both short-term and long-term dependencies. Our approach employs a
mixture of models, each with a different temporal range. These models are
combined by a learned gating mechanism capable of exerting different model
combinations given different contextual information. In empirical evaluations
on a public dataset and our own anonymized YouTube dataset, M3 consistently
outperforms state-of-the-art sequential recommendation methods.Comment: Accepted at WWW 201
Generative Temporal Models with Spatial Memory for Partially Observed Environments
In model-based reinforcement learning, generative and temporal models of
environments can be leveraged to boost agent performance, either by tuning the
agent's representations during training or via use as part of an explicit
planning mechanism. However, their application in practice has been limited to
simplistic environments, due to the difficulty of training such models in
larger, potentially partially-observed and 3D environments. In this work we
introduce a novel action-conditioned generative model of such challenging
environments. The model features a non-parametric spatial memory system in
which we store learned, disentangled representations of the environment.
Low-dimensional spatial updates are computed using a state-space model that
makes use of knowledge on the prior dynamics of the moving agent, and
high-dimensional visual observations are modelled with a Variational
Auto-Encoder. The result is a scalable architecture capable of performing
coherent predictions over hundreds of time steps across a range of partially
observed 2D and 3D environments.Comment: ICML 201
Twin Networks: Matching the Future for Sequence Generation
We propose a simple technique for encouraging generative RNNs to plan ahead.
We train a "backward" recurrent network to generate a given sequence in reverse
order, and we encourage states of the forward model to predict cotemporal
states of the backward model. The backward network is used only during
training, and plays no role during sampling or inference. We hypothesize that
our approach eases modeling of long-term dependencies by implicitly forcing the
forward states to hold information about the longer-term future (as contained
in the backward states). We show empirically that our approach achieves 9%
relative improvement for a speech recognition task, and achieves significant
improvement on a COCO caption generation task.Comment: 12 pages, 3 figures, published at ICLR 201
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