45,270 research outputs found
Deep Learning based Recommendation Systems
The usage of Internet applications, such as social networking and e-commerce is increasing exponentially, which leads to an increased offered content. Recommender systems help users filter out relevant content from a large pool of available content. The recommender systems play a vital role in today’s internet applications. Collaborative Filtering (CF) is one of the popular technique used to design recommendation systems. This technique recommends new content to users based on preferences that the user and similar users have. However, there are some shortcomings to current CF techniques, which affects negatively the performance of the recommendation models. In recent years, deep learning has achieved great success in natural language processing, computer vision and speech recognition. However, the use of deep learning in recommendation domain is relatively new. In this work, we tackle the shortcomings of collaborative filtering by using deep neural network techniques. Although some recent work has employed deep learning for recommendation, they only focused on modeling content descriptions, such as content information of items and auricular features of audios. Moreover, these models ignore the important factor of collaborative filtering, that is the user-item interaction function, but some models still employ matrix factorization, by using inner product on the latent features of items and users. In this project, the inner product is replaced by a neural network architecture, which learns an user-item interaction function from data. To handle any nonlinearities in the user-item interaction function, a multi-layer perceptron is used. Extensive experiments on two real-world datasets demonstrate improvements made by our model compared to existing popular collaborative filtering techniques. Empirical evidence shows deep learning based recommendation models have better performance
Graph Convolutional Matrix Completion
We consider matrix completion for recommender systems from the point of view
of link prediction on graphs. Interaction data such as movie ratings can be
represented by a bipartite user-item graph with labeled edges denoting observed
ratings. Building on recent progress in deep learning on graph-structured data,
we propose a graph auto-encoder framework based on differentiable message
passing on the bipartite interaction graph. Our model shows competitive
performance on standard collaborative filtering benchmarks. In settings where
complimentary feature information or structured data such as a social network
is available, our framework outperforms recent state-of-the-art methods.Comment: 9 pages, 3 figures, updated with additional experimental evaluatio
Neural Collaborative Filtering
In recent years, deep neural networks have yielded immense success on speech
recognition, computer vision and natural language processing. However, the
exploration of deep neural networks on recommender systems has received
relatively less scrutiny. In this work, we strive to develop techniques based
on neural networks to tackle the key problem in recommendation -- collaborative
filtering -- on the basis of implicit feedback. Although some recent work has
employed deep learning for recommendation, they primarily used it to model
auxiliary information, such as textual descriptions of items and acoustic
features of musics. When it comes to model the key factor in collaborative
filtering -- the interaction between user and item features, they still
resorted to matrix factorization and applied an inner product on the latent
features of users and items. By replacing the inner product with a neural
architecture that can learn an arbitrary function from data, we present a
general framework named NCF, short for Neural network-based Collaborative
Filtering. NCF is generic and can express and generalize matrix factorization
under its framework. To supercharge NCF modelling with non-linearities, we
propose to leverage a multi-layer perceptron to learn the user-item interaction
function. Extensive experiments on two real-world datasets show significant
improvements of our proposed NCF framework over the state-of-the-art methods.
Empirical evidence shows that using deeper layers of neural networks offers
better recommendation performance.Comment: 10 pages, 7 figure
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
Ask the GRU: Multi-Task Learning for Deep Text Recommendations
In a variety of application domains the content to be recommended to users is
associated with text. This includes research papers, movies with associated
plot summaries, news articles, blog posts, etc. Recommendation approaches based
on latent factor models can be extended naturally to leverage text by employing
an explicit mapping from text to factors. This enables recommendations for new,
unseen content, and may generalize better, since the factors for all items are
produced by a compactly-parametrized model. Previous work has used topic models
or averages of word embeddings for this mapping. In this paper we present a
method leveraging deep recurrent neural networks to encode the text sequence
into a latent vector, specifically gated recurrent units (GRUs) trained
end-to-end on the collaborative filtering task. For the task of scientific
paper recommendation, this yields models with significantly higher accuracy. In
cold-start scenarios, we beat the previous state-of-the-art, all of which
ignore word order. Performance is further improved by multi-task learning,
where the text encoder network is trained for a combination of content
recommendation and item metadata prediction. This regularizes the collaborative
filtering model, ameliorating the problem of sparsity of the observed rating
matrix.Comment: 8 page
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