1,116 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
RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback
Recent research has shown the advantages of using autoencoders based on deep
neural networks for collaborative filtering. In particular, the recently
proposed Mult-VAE model, which used the multinomial likelihood variational
autoencoders, has shown excellent results for top-N recommendations. In this
work, we propose the Recommender VAE (RecVAE) model that originates from our
research on regularization techniques for variational autoencoders. RecVAE
introduces several novel ideas to improve Mult-VAE, including a novel composite
prior distribution for the latent codes, a new approach to setting the
hyperparameter for the -VAE framework, and a new approach to training
based on alternating updates. In experimental evaluation, we show that RecVAE
significantly outperforms previously proposed autoencoder-based models,
including Mult-VAE and RaCT, across classical collaborative filtering datasets,
and present a detailed ablation study to assess our new developments. Code and
models are available at https://github.com/ilya-shenbin/RecVAE.Comment: In The Thirteenth ACM International Conference on Web Search and Data
Mining (WSDM '20), February 3-7, 2020, Houston, TX, USA. ACM, New York, NY,
USA, 9 page
Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes
Music preferences are strongly shaped by the cultural and socio-economic
background of the listener, which is reflected, to a considerable extent, in
country-specific music listening profiles. Previous work has already identified
several country-specific differences in the popularity distribution of music
artists listened to. In particular, what constitutes the "music mainstream"
strongly varies between countries. To complement and extend these results, the
article at hand delivers the following major contributions: First, using
state-of-the-art unsupervised learning techniques, we identify and thoroughly
investigate (1) country profiles of music preferences on the fine-grained level
of music tracks (in contrast to earlier work that relied on music preferences
on the artist level) and (2) country archetypes that subsume countries sharing
similar patterns of listening preferences. Second, we formulate four user
models that leverage the user's country information on music preferences. Among
others, we propose a user modeling approach to describe a music listener as a
vector of similarities over the identified country clusters or archetypes.
Third, we propose a context-aware music recommendation system that leverages
implicit user feedback, where context is defined via the four user models. More
precisely, it is a multi-layer generative model based on a variational
autoencoder, in which contextual features can influence recommendations through
a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation
system and user models on a real-world corpus of more than one billion
listening records of users around the world (out of which we use 369 million in
our experiments) and show its merits vis-a-vis state-of-the-art algorithms that
do not exploit this type of context information.Comment: 30 pages, 3 tables, 12 figure
Representation Learning for Texts and Graphs: A Unified Perspective on Efficiency, Multimodality, and Adaptability
[...] This thesis is situated between natural language processing and graph representation learning and investigates selected connections. First, we introduce matrix embeddings as an efficient text representation sensitive to word order. [...] Experiments with ten linguistic probing tasks, 11 supervised, and five unsupervised downstream tasks reveal that vector and matrix embeddings have complementary strengths and that a jointly trained hybrid model outperforms both. Second, a popular pretrained language model, BERT, is distilled into matrix embeddings. [...] The results on the GLUE benchmark show that these models are competitive with other recent contextualized language models while being more efficient in time and space. Third, we compare three model types for text classification: bag-of-words, sequence-, and graph-based models. Experiments on five datasets show that, surprisingly, a wide multilayer perceptron on top of a bag-of-words representation is competitive with recent graph-based approaches, questioning the necessity of graphs synthesized from the text. [...] Fourth, we investigate the connection between text and graph data in document-based recommender systems for citations and subject labels. Experiments on six datasets show that the title as side information improves the performance of autoencoder models. [...] We find that the meaning of item co-occurrence is crucial for the choice of input modalities and an appropriate model. Fifth, we introduce a generic framework for lifelong learning on evolving graphs in which new nodes, edges, and classes appear over time. [...] The results show that by reusing previous parameters in incremental training, it is possible to employ smaller history sizes with only a slight decrease in accuracy compared to training with complete history. Moreover, weighting the binary cross-entropy loss function is crucial to mitigate the problem of class imbalance when detecting newly emerging classes. [...
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