2 research outputs found
Deep Heterogeneous Autoencoders for Collaborative Filtering
This paper leverages heterogeneous auxiliary information to address the data
sparsity problem of recommender systems. We propose a model that learns a
shared feature space from heterogeneous data, such as item descriptions,
product tags and online purchase history, to obtain better predictions. Our
model consists of autoencoders, not only for numerical and categorical data,
but also for sequential data, which enables capturing user tastes, item
characteristics and the recent dynamics of user preference. We learn the
autoencoder architecture for each data source independently in order to better
model their statistical properties. Our evaluation on two MovieLens datasets
and an e-commerce dataset shows that mean average precision and recall improve
over state-of-the-art methods.Comment: Proceedings of the IEEE International Conference on Data Mining, pp.
1164-1169, Singapore, 201
Leveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems
With the exponentially increasing volume of online data, searching and
finding required information have become an extensive and time-consuming task.
Recommender Systems as a subclass of information retrieval and decision support
systems by providing personalized suggestions helping users access what they
need more efficiently. Among the different techniques for building a
recommender system, Collaborative Filtering (CF) is the most popular and
widespread approach. However, cold start and data sparsity are the fundamental
challenges ahead of implementing an effective CF-based recommender. Recent
successful developments in enhancing and implementing deep learning
architectures motivated many studies to propose deep learning-based solutions
for solving the recommenders' weak points. In this research, unlike the past
similar works about using deep learning architectures in recommender systems
that covered different techniques generally, we specifically provide a
comprehensive review of deep learning-based collaborative filtering recommender
systems. This in-depth filtering gives a clear overview of the level of
popularity, gaps, and ignored areas on leveraging deep learning techniques to
build CF-based systems as the most influential recommenders.Comment: 24 pages, 14 figure