417 research outputs found
A Variational Recurrent Neural Network for Session-Based Recommendations using Bayesian Personalized Ranking
This work introduces VRNN-BPR, a novel deep learning model, which is utilized in session-based Recommender systems tackling the data sparsity problem. The proposed model combines a Recurrent Neural Network with an amortized variational inference setup (AVI) and a Bayesian Personalized Ranking in order to produce predictions on sequence-based data and generate recommendations. The model is assessed using a large real-world dataset and the results demonstrate its superiority over current state-of-the-art techniques
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
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
Hierarchical Context enabled Recurrent Neural Network for Recommendation
A long user history inevitably reflects the transitions of personal interests
over time. The analyses on the user history require the robust sequential model
to anticipate the transitions and the decays of user interests. The user
history is often modeled by various RNN structures, but the RNN structures in
the recommendation system still suffer from the long-term dependency and the
interest drifts. To resolve these challenges, we suggest HCRNN with three
hierarchical contexts of the global, the local, and the temporary interests.
This structure is designed to withhold the global long-term interest of users,
to reflect the local sub-sequence interests, and to attend the temporary
interests of each transition. Besides, we propose a hierarchical context-based
gate structure to incorporate our \textit{interest drift assumption}. As we
suggest a new RNN structure, we support HCRNN with a complementary
\textit{bi-channel attention} structure to utilize hierarchical context. We
experimented the suggested structure on the sequential recommendation tasks
with CiteULike, MovieLens, and LastFM, and our model showed the best
performances in the sequential recommendations
BERT4Loc: BERT for Location -- POI Recommender System
Recommending points of interest is a difficult problem that requires precise
location information to be extracted from a location-based social media
platform. Another challenging and critical problem for such a location-aware
recommendation system is modelling users' preferences based on their historical
behaviors. We propose a location-aware recommender system based on
Bidirectional Encoder Representations from Transformers for the purpose of
providing users with location-based recommendations. The proposed model
incorporates location data and user preferences. When compared to predicting
the next item of interest (location) at each position in a sequence, our model
can provide the user with more relevant results. Extensive experiments on a
benchmark dataset demonstrate that our model consistently outperforms a variety
of state-of-the-art sequential models
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