99 research outputs found
Hybrid Recommender Systems for Next Purchase Prediction Based on Optimal Combination Weights
Recommender systems (RS) play a key role in e-commerce by preselecting presumably interesting products for customers. Hybrid RSs using a weighted average of individual RSsâ predictions have been widely adopted for improving accuracy and robustness over individual RSs. While for regression tasks, approaches to estimate optimal weighting schemes based on individual RSsâ out-of-sample errors exist, there is scant literature in classification settings. Class prediction is important for RSs in e-commerce, as here item purchases are to be predicted. We propose a method for estimating weighting schemes to combine classifying RSs based on the variance-covariance structures of the errors of individual models' probability scores. We evaluate the approach on a large real-world ecommerce data set from a European telecommunications provider, where it shows superior accuracy compared to the best individual model as well as a weighting scheme that averages the predictions using equal weights
Applying Optimal Weight Combination in Hybrid Recommender Systems
We propose a method for learning weighting schemes in weighted hybrid recommender systems (RS) that is based on statistical forecast and portfolio theory. An RS predicts the future preference of a set of items for a user, and recommends the top items. A hybrid RS combines individual RS in making the predictions. To determine the weighting of individual RS, we learn so-called optimal weights from the covariance matrix of available error data of individual RS that minimize the error of a combined RS. We test the method on the well-known MovieLens 1M dataset, and, contrary to the âforecast combination puzzleâ, stating that a simple average (SA) weighting typically outperforms learned weights, the out-of-sample results show that the learned weights consistently outperform the individually best RS as well as an SA combination
A Unifying View on Blind Source Separation of Convolutive Mixtures based on Independent Component Analysis
In many daily-life scenarios, acoustic sources recorded in an enclosure can
only be observed with other interfering sources. Hence, convolutive Blind
Source Separation (BSS) is a central problem in audio signal processing.
Methods based on Independent Component Analysis (ICA) are especially important
in this field as they require only few and weak assumptions and allow for
blindness regarding the original source signals and the acoustic propagation
path. Most of the currently used algorithms belong to one of the following
three families: Frequency Domain ICA (FD-ICA), Independent Vector Analysis
(IVA), and TRIple-N Independent component analysis for CONvolutive mixtures
(TRINICON). While the relation between ICA, FD-ICA and IVA becomes apparent due
to their construction, the relation to TRINICON is not well established yet.
This paper fills this gap by providing an in-depth treatment of the common
building blocks of these algorithms and their differences, and thus provides a
common framework for all considered algorithms
Localizing Spatial Information in Neural Spatiospectral Filters
Beamforming for multichannel speech enhancement relies on the estimation of
spatial characteristics of the acoustic scene. In its simplest form, the
delay-and-sum beamformer (DSB) introduces a time delay to all channels to align
the desired signal components for constructive superposition. Recent
investigations of neural spatiospectral filtering revealed that these filters
can be characterized by a beampattern similar to one of traditional
beamformers, which shows that artificial neural networks can learn and
explicitly represent spatial structure. Using the Complex-valued Spatial
Autoencoder (COSPA) as an exemplary neural spatiospectral filter for
multichannel speech enhancement, we investigate where and how such networks
represent spatial information. We show via clustering that for COSPA the
spatial information is represented by the features generated by a gated
recurrent unit (GRU) layer that has access to all channels simultaneously and
that these features are not source -- but only direction of arrival-dependent.Comment: Submitted to the 31st European Signal Processing Conference (EUSIPCO
2023), Helsinki, Finland. 5 pages, 3 figure
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