16,183 research outputs found
How to Retrain Recommender System? A Sequential Meta-Learning Method
Practical recommender systems need be periodically retrained to refresh the
model with new interaction data. To pursue high model fidelity, it is usually
desirable to retrain the model on both historical and new data, since it can
account for both long-term and short-term user preference. However, a full
model retraining could be very time-consuming and memory-costly, especially
when the scale of historical data is large. In this work, we study the model
retraining mechanism for recommender systems, a topic of high practical values
but has been relatively little explored in the research community.
Our first belief is that retraining the model on historical data is
unnecessary, since the model has been trained on it before. Nevertheless,
normal training on new data only may easily cause overfitting and forgetting
issues, since the new data is of a smaller scale and contains fewer information
on long-term user preference. To address this dilemma, we propose a new
training method, aiming to abandon the historical data during retraining
through learning to transfer the past training experience. Specifically, we
design a neural network-based transfer component, which transforms the old
model to a new model that is tailored for future recommendations. To learn the
transfer component well, we optimize the "future performance" -- i.e., the
recommendation accuracy evaluated in the next time period. Our Sequential
Meta-Learning(SML) method offers a general training paradigm that is applicable
to any differentiable model. We demonstrate SML on matrix factorization and
conduct experiments on two real-world datasets. Empirical results show that SML
not only achieves significant speed-up, but also outperforms the full model
retraining in recommendation accuracy, validating the effectiveness of our
proposals. We release our codes at: https://github.com/zyang1580/SML.Comment: Appear in SIGIR 202
Deep Item-based Collaborative Filtering for Top-N Recommendation
Item-based Collaborative Filtering(short for ICF) has been widely adopted in
recommender systems in industry, owing to its strength in user interest
modeling and ease in online personalization. By constructing a user's profile
with the items that the user has consumed, ICF recommends items that are
similar to the user's profile. With the prevalence of machine learning in
recent years, significant processes have been made for ICF by learning item
similarity (or representation) from data. Nevertheless, we argue that most
existing works have only considered linear and shallow relationship between
items, which are insufficient to capture the complicated decision-making
process of users.
In this work, we propose a more expressive ICF solution by accounting for the
nonlinear and higher-order relationship among items. Going beyond modeling only
the second-order interaction (e.g. similarity) between two items, we
additionally consider the interaction among all interacted item pairs by using
nonlinear neural networks. Through this way, we can effectively model the
higher-order relationship among items, capturing more complicated effects in
user decision-making. For example, it can differentiate which historical
itemsets in a user's profile are more important in affecting the user to make a
purchase decision on an item. We treat this solution as a deep variant of ICF,
thus term it as DeepICF. To justify our proposal, we perform empirical studies
on two public datasets from MovieLens and Pinterest. Extensive experiments
verify the highly positive effect of higher-order item interaction modeling
with nonlinear neural networks. Moreover, we demonstrate that by more
fine-grained second-order interaction modeling with attention network, the
performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI
A Projected Gradient Descent Method for CRF Inference allowing End-To-End Training of Arbitrary Pairwise Potentials
Are we using the right potential functions in the Conditional Random Field
models that are popular in the Vision community? Semantic segmentation and
other pixel-level labelling tasks have made significant progress recently due
to the deep learning paradigm. However, most state-of-the-art structured
prediction methods also include a random field model with a hand-crafted
Gaussian potential to model spatial priors, label consistencies and
feature-based image conditioning.
In this paper, we challenge this view by developing a new inference and
learning framework which can learn pairwise CRF potentials restricted only by
their dependence on the image pixel values and the size of the support. Both
standard spatial and high-dimensional bilateral kernels are considered. Our
framework is based on the observation that CRF inference can be achieved via
projected gradient descent and consequently, can easily be integrated in deep
neural networks to allow for end-to-end training. It is empirically
demonstrated that such learned potentials can improve segmentation accuracy and
that certain label class interactions are indeed better modelled by a
non-Gaussian potential. In addition, we compare our inference method to the
commonly used mean-field algorithm. Our framework is evaluated on several
public benchmarks for semantic segmentation with improved performance compared
to previous state-of-the-art CNN+CRF models.Comment: Presented at EMMCVPR 2017 conferenc
Neural Graph Collaborative Filtering
Learning vector representations (aka. embeddings) of users and items lies at
the core of modern recommender systems. Ranging from early matrix factorization
to recently emerged deep learning based methods, existing efforts typically
obtain a user's (or an item's) embedding by mapping from pre-existing features
that describe the user (or the item), such as ID and attributes. We argue that
an inherent drawback of such methods is that, the collaborative signal, which
is latent in user-item interactions, is not encoded in the embedding process.
As such, the resultant embeddings may not be sufficient to capture the
collaborative filtering effect.
In this work, we propose to integrate the user-item interactions -- more
specifically the bipartite graph structure -- into the embedding process. We
develop a new recommendation framework Neural Graph Collaborative Filtering
(NGCF), which exploits the user-item graph structure by propagating embeddings
on it. This leads to the expressive modeling of high-order connectivity in
user-item graph, effectively injecting the collaborative signal into the
embedding process in an explicit manner. We conduct extensive experiments on
three public benchmarks, demonstrating significant improvements over several
state-of-the-art models like HOP-Rec and Collaborative Memory Network. Further
analysis verifies the importance of embedding propagation for learning better
user and item representations, justifying the rationality and effectiveness of
NGCF. Codes are available at
https://github.com/xiangwang1223/neural_graph_collaborative_filtering.Comment: SIGIR 2019; the latest version of NGCF paper, which is distinct from
the version published in ACM Digital Librar
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