Deep Learning for Recommender Systems Tutorial slides presented at ACM RecSys 2017 in Como, Italy. ]]>

Deep Learning for Recommender Systems Tutorial slides presented at ACM RecSys 2017 in Como, Italy. ]]>

An overview of some deep learning methods for recommender systems along with an intro to the relevant deep learning methods such as convolutional neural networks (CNN's), recurrent neural networks (RNN's), autoencoders, restricted boltzmann machines (RBM's) and more.]]>

An overview of some deep learning methods for recommender systems along with an intro to the relevant deep learning methods such as convolutional neural networks (CNN's), recurrent neural networks (RNN's), autoencoders, restricted boltzmann machines (RBM's) and more.]]>

The slides from the Machine Learning Summers School 2015 in Sydney on Machine Learning for Recommender Systems. Collaborative filtering algorithms, Context-aware methods, Restricted Boltzmann Machines, Recurrent Neural Networks, Tensor Factorization, etc.]]>

The slides from the Machine Learning Summers School 2015 in Sydney on Machine Learning for Recommender Systems. Collaborative filtering algorithms, Context-aware methods, Restricted Boltzmann Machines, Recurrent Neural Networks, Tensor Factorization, etc.]]>

Slides from my talk at the RecSys Stammtisch at SoundCloud in Berlin. The presentation is split in two part one focusing on ranking and relevance and one on diversity and how to achieve it using genres. We introduce a novel diversity metric called Binomial Diversity.]]>

Slides from my talk at the RecSys Stammtisch at SoundCloud in Berlin. The presentation is split in two part one focusing on ranking and relevance and one on diversity and how to achieve it using genres. We introduce a novel diversity metric called Binomial Diversity.]]>

The slides from the Learning to Rank for Recommender Systems tutorial given at ACM RecSys 2013 in Hong Kong by Alexandros Karatzoglou, Linas Baltrunas and Yue Shi. ]]>

The slides from the Learning to Rank for Recommender Systems tutorial given at ACM RecSys 2013 in Hong Kong by Alexandros Karatzoglou, Linas Baltrunas and Yue Shi. ]]>

Recommenders Systems tutorial slides from the European Summer School of Information Retrieval (ESSIR). Covers basic ideas on Collaborative Filtering, Content-based methods, Matrix Factorization, Restricted Boltzmann Machines, Ranking, Diversity. The slides include material from Xavier Amatriain, Saul Vargas and Linas Baltrunas. ]]>

Recommenders Systems tutorial slides from the European Summer School of Information Retrieval (ESSIR). Covers basic ideas on Collaborative Filtering, Content-based methods, Matrix Factorization, Restricted Boltzmann Machines, Ranking, Diversity. The slides include material from Xavier Amatriain, Saul Vargas and Linas Baltrunas. ]]>

Slides from RecSys 2010 presentation. Context has been recognized as an important factor to con- sider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Ma- trix Factorization do not provide a straightforward way of integrating context information into the model. In this work, we introduce a Collaborative Filtering method based on Tensor Factorization, a generalization of Matrix Factoriza- tion that allows for a flexible and generic integration of con- textual information by modeling the data as a User-Item- Context N-dimensional tensor instead of the traditional 2D User-Item matrix. In the proposed model, called Multiverse Recommendation, different types of context are considered as additional dimensions in the representation of the data as a tensor]]>

Slides from RecSys 2010 presentation. Context has been recognized as an important factor to con- sider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Ma- trix Factorization do not provide a straightforward way of integrating context information into the model. In this work, we introduce a Collaborative Filtering method based on Tensor Factorization, a generalization of Matrix Factoriza- tion that allows for a flexible and generic integration of con- textual information by modeling the data as a User-Item- Context N-dimensional tensor instead of the traditional 2D User-Item matrix. In the proposed model, called Multiverse Recommendation, different types of context are considered as additional dimensions in the representation of the data as a tensor]]>

Slides from the presentation of TFMAP at SIGIR 2012. TFMAP, is a Collaborative Filtering model that directly maximizes Mean Average Precision with the aim of creating an optimally ranked list of items for individual users under a given context. TFMAP uses tensor factorization to model implicit feedback data (e.g., purchases, clicks) along with contextual information]]>

Slides from the presentation of TFMAP at SIGIR 2012. TFMAP, is a Collaborative Filtering model that directly maximizes Mean Average Precision with the aim of creating an optimally ranked list of items for individual users under a given context. TFMAP uses tensor factorization to model implicit feedback data (e.g., purchases, clicks) along with contextual information]]>

RecSys presentation slides of CLiMF, a Collaborative Filtering algorithm based on a novel ranking algorithm ]]>

RecSys presentation slides of CLiMF, a Collaborative Filtering algorithm based on a novel ranking algorithm ]]>

Slides for a Machine Learning Course in R, includes an introduction to R and several ML methods for classification, regression, clustering and dimensionality reduction.]]>

Slides for a Machine Learning Course in R, includes an introduction to R and several ML methods for classification, regression, clustering and dimensionality reduction.]]>