2,845 research outputs found
Learning and Transferring IDs Representation in E-commerce
Many machine intelligence techniques are developed in E-commerce and one of
the most essential components is the representation of IDs, including user ID,
item ID, product ID, store ID, brand ID, category ID etc. The classical
encoding based methods (like one-hot encoding) are inefficient in that it
suffers sparsity problems due to its high dimension, and it cannot reflect the
relationships among IDs, either homogeneous or heterogeneous ones. In this
paper, we propose an embedding based framework to learn and transfer the
representation of IDs. As the implicit feedbacks of users, a tremendous amount
of item ID sequences can be easily collected from the interactive sessions. By
jointly using these informative sequences and the structural connections among
IDs, all types of IDs can be embedded into one low-dimensional semantic space.
Subsequently, the learned representations are utilized and transferred in four
scenarios: (i) measuring the similarity between items, (ii) transferring from
seen items to unseen items, (iii) transferring across different domains, (iv)
transferring across different tasks. We deploy and evaluate the proposed
approach in Hema App and the results validate its effectiveness.Comment: KDD'18, 9 page
Learning Tree-based Deep Model for Recommender Systems
Model-based methods for recommender systems have been studied extensively in
recent years. In systems with large corpus, however, the calculation cost for
the learnt model to predict all user-item preferences is tremendous, which
makes full corpus retrieval extremely difficult. To overcome the calculation
barriers, models such as matrix factorization resort to inner product form
(i.e., model user-item preference as the inner product of user, item latent
factors) and indexes to facilitate efficient approximate k-nearest neighbor
searches. However, it still remains challenging to incorporate more expressive
interaction forms between user and item features, e.g., interactions through
deep neural networks, because of the calculation cost.
In this paper, we focus on the problem of introducing arbitrary advanced
models to recommender systems with large corpus. We propose a novel tree-based
method which can provide logarithmic complexity w.r.t. corpus size even with
more expressive models such as deep neural networks. Our main idea is to
predict user interests from coarse to fine by traversing tree nodes in a
top-down fashion and making decisions for each user-node pair. We also show
that the tree structure can be jointly learnt towards better compatibility with
users' interest distribution and hence facilitate both training and prediction.
Experimental evaluations with two large-scale real-world datasets show that the
proposed method significantly outperforms traditional methods. Online A/B test
results in Taobao display advertising platform also demonstrate the
effectiveness of the proposed method in production environments.Comment: Accepted by KDD 201
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
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
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