414 research outputs found
Support Vector Machines in R
Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language. Currently four R packages contain SVM related software. The purpose of this paper is to present and compare these implementations.
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
Session-based recommendations are highly relevant in many modern on-line
services (e.g. e-commerce, video streaming) and recommendation settings.
Recently, Recurrent Neural Networks have been shown to perform very well in
session-based settings. While in many session-based recommendation domains user
identifiers are hard to come by, there are also domains in which user profiles
are readily available. We propose a seamless way to personalize RNN models with
cross-session information transfer and devise a Hierarchical RNN model that
relays end evolves latent hidden states of the RNNs across user sessions.
Results on two industry datasets show large improvements over the session-only
RNNs
kernlab - An S4 Package for Kernel Methods in R
kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R's new S4 ob ject model and provides a framework for creating and using kernel-based algorithms. The package contains dot product primitives (kernels), implementations of support vector machines and the relevance vector machine, Gaussian processes, a ranking algorithm, kernel PCA, kernel CCA, and a spectral clustering algorithm. Moreover it provides a general purpose quadratic programming solver, and an incomplete Cholesky decomposition method.
On Interpretation and Measurement of Soft Attributes for Recommendation
We address how to robustly interpret natural language refinements (or critiques) in recommender systems. In particular, in human-human recommendation settings people frequently use soft attributes to express preferences about items, including concepts like the originality of a movie plot, the noisiness of a venue, or the complexity of a recipe. While binary tagging is extensively studied in the context of recommender systems, soft attributes often involve subjective and contextual aspects, which cannot be captured reliably in this way, nor be represented as objective binary truth in a knowledge base. This also adds important considerations when measuring soft attribute ranking. We propose a more natural representation as personalized relative statements, rather than as absolute item properties. We present novel data collection techniques and evaluation approaches, and a new public dataset. We also propose a set of scoring approaches, from unsupervised to weakly supervised to fully supervised, as a step towards interpreting and acting upon soft attribute based critiques.publishedVersio
User-Item Reciprocity in Recommender Systems: Incentivizing the Crowd
Data consumption has changed significantly in the last 10
years. The digital revolution and the Internet has brought an abundance
of information to users. Recommender systems are a popular means of
finding content that is both relevant and personalized. However, today’s
users require better recommender systems, able of producing continuous
data feeds keeping up with their instantaneous and mobile needs. The
CrowdRec project addresses this demand by providing context-aware,
resource-combining, socially-informed, interactive and scalable recommendations.
The key insight of CrowdRec is that, in order to achieve
the dense, high-quality, timely information required for such systems, it
is necessary to move from passive user data collection, to more active
techniques fostering user engagement. For this purpose, CrowdRec activates
the crowd, soliciting input and feedback from the wider communit
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