142 research outputs found
Collaborative Deep Learning for Recommender Systems
Collaborative filtering (CF) is a successful approach commonly used by many
recommender systems. Conventional CF-based methods use the ratings given to
items by users as the sole source of information for learning to make
recommendation. However, the ratings are often very sparse in many
applications, causing CF-based methods to degrade significantly in their
recommendation performance. To address this sparsity problem, auxiliary
information such as item content information may be utilized. Collaborative
topic regression (CTR) is an appealing recent method taking this approach which
tightly couples the two components that learn from two different sources of
information. Nevertheless, the latent representation learned by CTR may not be
very effective when the auxiliary information is very sparse. To address this
problem, we generalize recent advances in deep learning from i.i.d. input to
non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian
model called collaborative deep learning (CDL), which jointly performs deep
representation learning for the content information and collaborative filtering
for the ratings (feedback) matrix. Extensive experiments on three real-world
datasets from different domains show that CDL can significantly advance the
state of the art
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
Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems
Recently, user-oriented auto-encoders (UAEs) have been widely used in
recommender systems to learn semantic representations of users based on their
historical ratings. However, since latent item variables are not modeled in
UAE, it is difficult to utilize the widely available item content information
when ratings are sparse. In addition, whenever new items arrive, we need to
wait for collecting rating data for these items and retrain the UAE from
scratch, which is inefficient in practice. Aiming to address the above two
problems simultaneously, we propose a mutually-regularized dual collaborative
variational auto-encoder (MD-CVAE) for recommendation. First, by replacing
randomly initialized last layer weights of the vanilla UAE with stacked latent
item embeddings, MD-CVAE integrates two heterogeneous information sources,
i.e., item content and user ratings, into the same principled variational
framework where the weights of UAE are regularized by item content such that
convergence to a non-optima due to data sparsity can be avoided. In addition,
the regularization is mutual in that user ratings can also help the dual item
content module learn more recommendation-oriented item content embeddings.
Finally, we propose a symmetric inference strategy for MD-CVAE where the first
layer weights of the UAE encoder are tied to the latent item embeddings of the
UAE decoder. Through this strategy, no retraining is required to recommend
newly introduced items. Empirical studies show the effectiveness of MD-CVAE in
both normal and cold-start scenarios. Codes are available at
https://github.com/yaochenzhu/MD-CVAE
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