5,294 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
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling
We address the problem of efficient exploration for transition model learning
in the relational model-based reinforcement learning setting without extrinsic
goals or rewards. Inspired by human curiosity, we propose goal-literal babbling
(GLIB), a simple and general method for exploration in such problems. GLIB
samples relational conjunctive goals that can be understood as specific,
targeted effects that the agent would like to achieve in the world, and plans
to achieve these goals using the transition model being learned. We provide
theoretical guarantees showing that exploration with GLIB will converge almost
surely to the ground truth model. Experimentally, we find GLIB to strongly
outperform existing methods in both prediction and planning on a range of
tasks, encompassing standard PDDL and PPDDL planning benchmarks and a robotic
manipulation task implemented in the PyBullet physics simulator. Video:
https://youtu.be/F6lmrPT6TOY Code: https://git.io/JIsTBComment: AAAI 202
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