1,751 research outputs found
Embedding Words as Distributions with a Bayesian Skip-gram Model
We introduce a method for embedding words as probability densities in a
low-dimensional space. Rather than assuming that a word embedding is fixed
across the entire text collection, as in standard word embedding methods, in
our Bayesian model we generate it from a word-specific prior density for each
occurrence of a given word. Intuitively, for each word, the prior density
encodes the distribution of its potential 'meanings'. These prior densities are
conceptually similar to Gaussian embeddings. Interestingly, unlike the Gaussian
embeddings, we can also obtain context-specific densities: they encode
uncertainty about the sense of a word given its context and correspond to
posterior distributions within our model. The context-dependent densities have
many potential applications: for example, we show that they can be directly
used in the lexical substitution task. We describe an effective estimation
method based on the variational autoencoding framework. We also demonstrate
that our embeddings achieve competitive results on standard benchmarks.Comment: COLING 2018. For the associated code, see
https://github.com/ixlan/BS
Variational Bayesian Context-aware Representation for Grocery Recommendation
Grocery recommendation is an important recommendation use-case, which aims to predict which items a user might choose to buy in the future, based on their shopping history. However, existing methods only represent each user and item by single deterministic points in a low-dimensional continuous space. In addition, most of these methods are trained by maximizing the co-occurrence likelihood with a simple Skip-gram-based formulation, which limits the expressive ability of their embeddings and the resulting recommendation performance. In this paper, we propose the Variational Bayesian Context-Aware Representation (VBCAR) model for grocery recommendation, which is a novel variational Bayesian model that learns the user and item latent vectors by leveraging basket context information from past user-item interactions. We train our VBCAR model based on the Bayesian Skip-gram framework coupled with the amortized variational inference so that it can learn more expressive latent representations that integrate both the non-linearity and Bayesian behaviour. Experiments conducted on a large real-world grocery recommendation dataset show that our proposed VBCAR model can significantly outperform existing state-of-the-art grocery recommendation methods
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