1,055 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Modeling Dynamic User Interests: A Neural Matrix Factorization Approach
In recent years, there has been significant interest in understanding users'
online content consumption patterns. But, the unstructured, high-dimensional,
and dynamic nature of such data makes extracting valuable insights challenging.
Here we propose a model that combines the simplicity of matrix factorization
with the flexibility of neural networks to efficiently extract nonlinear
patterns from massive text data collections relevant to consumers' online
consumption patterns. Our model decomposes a user's content consumption journey
into nonlinear user and content factors that are used to model their dynamic
interests. This natural decomposition allows us to summarize each user's
content consumption journey with a dynamic probabilistic weighting over a set
of underlying content attributes. The model is fast to estimate, easy to
interpret and can harness external data sources as an empirical prior. These
advantages make our method well suited to the challenges posed by modern
datasets. We use our model to understand the dynamic news consumption interests
of Boston Globe readers over five years. Thorough qualitative studies,
including a crowdsourced evaluation, highlight our model's ability to
accurately identify nuanced and coherent consumption patterns. These results
are supported by our model's superior and robust predictive performance over
several competitive baseline methods
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