January 2016 – Eliezer Silva

Probabilistic models for Recommender systems (part I): Probabilistic Matrix Factorization

January 25, 2016June 17, 2020 Eliezer Silva1 Comment

In Recommender Systems design we are faced with the following problem: given incomplete information about users preference, content information, user-items rating and contextual information, learn the user preference and suggest new items for users based on features as:

previous pattern of items preference of the user;
preference of users with similar rating pattern;
contextual information: location, time (of the day, week, month, year), social network.

This is usually formulated as a matrix completion problem using Matrix Factorization techniques to offer a optimal solution. Is this case latent features for users and item are inferred from the observed/rated items for each user, and from this latent features the missing entries are estimated. One major modelling tool for this problem is probabilistic modelling, and there are many proposals in the literature of different Probabillistic Matrix Factorization approaches. We will briefly discuss some of this models, starting with the seminal paper: Probabilistic Matrix Factorization (PMF) – [Salakhutdinov and Mnih, 2008, NIPS].

Continue reading →

Hidden Markov Models (part I): recurrence equations for filtering and prediction

January 18, 2016March 13, 2016 Eliezer Silva1 Comment

This semester I will be attending the doctoral course MA8702 – Advanced Modern Statistical Methods with the excellent Prof. Håvard Rue. It will be course about statistical models defined over sparse structures (chains and graphs). We will start with Hidden Markov Chains and after go to Gaussian Markov Random Fields, Latent Gaussian Models and approximate inference with Integrated Nested Laplace Approximation (INLA). All this models are interesting for my research objective of developing sound latent models for recommender systems and I am really happy of taking this course with this great teacher and researcher. So, I will try to cover some of the material of the course, starting from what we saw in the first lecture: exact recurrence for Hidden Markov Chains and dynamic programming. In other words, general equations for predictions, filtering, smoothing, sampling, mode and marginal likelihood calculation of state-space model with latent variables. We will start by introduction the general model and specifying how to obtain the prediction and filtering equation.

Markovian property: $\pi(x_1,x_2,\cdots,x_T)=\pi(x_{1:T})=\prod_{t=1}^{t=T}\pi(x_t|x_{t-1})$ , with $\pi(x_1|x_0)=\pi(x_1)$
$y_t$ are observed and $x_t$ are latent, so $\pi(y_t|x_t)$ is always known.
If we know $x_{t-1}$ than no other variable will add any information to the conditional distribution of $x_t$ .

Continue reading “Hidden Markov Models (part I): recurrence equations for filtering and prediction” →

Script for searching and web crawling Springer Books

January 17, 2016January 18, 2016 Eliezer SilvaLeave a comment

So, some of you may have heard about the free e-books frenzy at Springer (unfortunately, not working any more ). I jumped on the train of people trying to find the goodies on the website and ended up developing some tools to help me with that. Even after the freebies ended I kept on improving this tool because it is useful also in my day-to-day job of searching for bibliography and storing those PDF’s. So this script can basically navigate through a search result (passed to the script as a link to the search result) and download all books available (even if it consist of many pages results). This is also useful for downloading all books from a series (for example, springer series in statistics). In the future I intend to update the script including download of chapters of books and research articles. This script is specially useful if your university have access to Springer Books. (full code in https://gist.github.com/zehsilva/d890837cdfd4a20ee8a5#file-download-books-from-rss-springer-py) Continue reading “Script for searching and web crawling Springer Books” →