93,171 research outputs found
LambdaOpt: Learn to Regularize Recommender Models in Finer Levels
Recommendation models mainly deal with categorical variables, such as
user/item ID and attributes. Besides the high-cardinality issue, the
interactions among such categorical variables are usually long-tailed, with the
head made up of highly frequent values and a long tail of rare ones. This
phenomenon results in the data sparsity issue, making it essential to
regularize the models to ensure generalization. The common practice is to
employ grid search to manually tune regularization hyperparameters based on the
validation data. However, it requires non-trivial efforts and large computation
resources to search the whole candidate space; even so, it may not lead to the
optimal choice, for which different parameters should have different
regularization strengths. In this paper, we propose a hyperparameter
optimization method, LambdaOpt, which automatically and adaptively enforces
regularization during training. Specifically, it updates the regularization
coefficients based on the performance of validation data. With LambdaOpt, the
notorious tuning of regularization hyperparameters can be avoided; more
importantly, it allows fine-grained regularization (i.e. each parameter can
have an individualized regularization coefficient), leading to better
generalized models. We show how to employ LambdaOpt on matrix factorization, a
classical model that is representative of a large family of recommender models.
Extensive experiments on two public benchmarks demonstrate the superiority of
our method in boosting the performance of top-K recommendation.Comment: Accepted by KDD 201
From Social Simulation to Integrative System Design
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c
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