## GIR’17 and visiting RMIT

Our position paper called “Poisson Factorization Models for Spatiotemporal Retrieval”, joint work with Dirk Ahlers, got accepted at the 11th Workshop on Geographic Information Retrieval (GIR’17). In this work, we discuss some modelling ideas and possibilities for advancing spatiotemporal retrieval using Poisson factorization models, especially in scenarios where we have multiple sources of count or implicit spatiotemporal user data. Unfortunately, I will not be able to attend the workshop (but Dirk will be there), because I am now in Melbourne, Australia, and will stay here for 3 months, participating as visiting graduate student in a project with the IR group at RMIT. In particular, I will be working with Dr Yongli Ren and Prof Mark Sanderson, developing joint probabilistic models for spatiotemporal user data for indoor spaces recommendations (they have a very interesting dataset that I am curious to explore). Hopefully, in the next couple of months, I will continue working on nice probabilistic models for recommender system, but incorporating many new and interesting ideas related to location and time.

## Post-conference: ECML-PKDD 2017

ECML-PKDD 2017 was very pleasant and nice. Skopje was an unexpected surprise. I am happy with each new conference that I attend, always meeting new people doing very good research. The community there was very nice in general!

I presented my paper at Matrix and Tensor Factorization session, and I was particularly happy with that, because even though the application we are working is recommender systems, we are focusing on the methods and proposing new factorization methods and models. Later in the night, we had the poster (poster-ecml2017) session at the Macedonian Opera & Ballet and afterward headed to the wine festival, just outside.

For those interested, my presentation slides here:

## Recommender Systems and Deep Learning: paper links

This semester I will be advising some master students on their final project. At this point, they don’t select a specific topic but should look into a given area to find specific research question and some of them will definitely work on Deep Learning and Recommender Systems. Especially because we (the NTNU-AILab group) had a very nice experience last year where one master student doing work on RNN for session-based recommendation managed to have a work accepted at DLRS 2017. So, I decided to make a small selection of the papers related to this topic, focusing on WSDM, WWW, KDD, CIKM, RecSys, ICLR, DLRS and some other specific conferences in the last three years (2015,2016 and 2017). The result is a list of 45 papers, with many distinct ideas, but also some common threads (Matrix Factorization to CNN or LSTM, Session-based methods using RNN, etc). We will not discuss the different ideas, but I will just post the link here because some people might be interested in that.

https://github.com/zehsilva/recsys-deeplearning-info

## Paper accepted at European Conference on Machine Learning (ECML-PKDD) 2017

We have a paper accepted at ECML-PKDD 2017: “Content-Based Social Recommendation with Poisson Matrix Factorization” (Eliezer de Souza da Silva, Helge Langseth and Heri Ramampiaro). This is our first full paper resulting from our research on Poisson factorization and integration of multiple sources of information in a single recommendation model. If you have interest on the paper please email me and I will be happy to discuss.

Also, I am uploading the supplement of the paper here (you can find it also on my publications page)

Supplementary material for: “Content-Based Social
Recommendation with Poisson Matrix Factorization”

## Hidden Markov Models (part II): forward-backward algorithm for marginal conditional probability of the states

(in the same series HMM (part I): recurrence equations for filtering and prediction)

Consider a Hidden Markov Model (HMM) with hidden states $x_t$ (for $t \in {1, 2, \cdots, T}$), initial probability $p(x_1)$, observed states $y_t$, transition probability $p(x_t|x_{t-1})$ and observation model $p(y_t|x_t)$. This model can be factorized as
$p(x_{1:T},y_{1:T}) = p(y_1|x_1)p(x_1)\prod_{t=2}^{t=T}p(y_t|x_t)p(x_t|x_{t-1})$. We will use the notation $X=x_{1:T}$ to represent the set $X=\{x_1,x_2,\cdots,x_T\}$.
In this post we will present the details of the method to find the smoothing distribution $p(x_t|y_{1:T})$ of a HMM, given a set of observations $y_{1:T}$:
Our starting point is the marginal probability $p(x_t|y_{1:T})$ of $x_t$ given all the observations $y_{1:T}$.

\begin{aligned} p(x_t|y_{1:T}) &= \frac{p(x_t,y_{1:T})}{p(y_{1:T})} \\ &= \frac{p(x_t,y_{1:t},y_{(t+1):T})}{p(y_{1:T})}\\ &= \underbrace{p(y_{(t+1):T}|x_t)}_{\beta_t(x_t)}\underbrace{p(x_t,y_{1:t})}_{\alpha_t(x_t)}\frac{1}{p(y_{1:T})} \\ &= \frac{\alpha_t(x_t) \beta_t(x_t)}{p(y_{1:T})} \end{aligned}

## Sampling from Dirichlet Distribution using Gamma distributed samples

There is an algorithm to generate Dirichlet samples using a sampler for Gamma distribution for any $\alpha > 0$ and $\beta > 0$. We will generate Gamma distributed variables $z_k \sim \text{gamma}(\alpha_k,1)$, for $k \in {1,\cdots,d}$, and do the following variable transformation to get Dirichlet samples $x_k = \frac{z_k}{\sum_k z_k}$. First we should demonstrate that this transformation results in Dirichlet distributed samples.

Consider the following tranformation $(z_1,\cdots,z_d) \leftarrow (x_1,\cdots,x_d,v)$, where $x_k = \frac{z_k}{\sum_k z_k}$ and $v = {\sum_k z_k}$. We can rewrite this transformation as $(x_1,\cdots,x_d,v)=h(z_1,\cdots,z_d)$, where $x_k = \frac{z_k}{v}$ and $v = {\sum_k z_k}$. Also we can imediatly calculate the inverse transformation $(z_1,\cdots,z_d)=h^{-1}(x_1,\cdots,x_d,v)$, with $z_k=v x_k$. From the transformation definition we know that ${\sum_{k=1}^d x_k=1}$, implying that $x_d = 1-\sum_{k=1}^{d-1} x_k$ and $z_d=v(1-\sum_{k=1}^{d-1}x_k)$.

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

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

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

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 $x_t$.

So, as we approach the end of 2015 I want to post some personal and academic updates.

• During the whole year I have been revising the literature on a couple of areas that interest me, that includes: hashing algorithms for massive data analysis, computational geometry (coresets, higher order voronoi diagrams, sketches, high dimensional geometry), computational social science, network science, undecibility of theories, random metric models, random matrix theory, deep learning theory, approximate inference algorithms and graphical models for recommender systems. Some of the future blog posts will take a look on some of these subjects.
• Half of the year I was involved in a very interesting project at Brazilian Institute of Geography and Statistics (IBGE), planning and implementing the supporting systems for surveys research (using Windows Phone). Even though I was excited to move back to academy, it was nice to be in this kind of project.
• I was accepted as a PhD Fellow (stipendiat) in the Department of Computer and Information Science at Norwegian University of Science and Technology (NTNU) with a project about probabilistic models and algorithms for recommender systems, working with Prof. Helge Langseth and Heri Ramampiaro. This was a big change for me and Juliana, who was pregnant, but readily accepted the challenge of leaving hot Rio and coming to cold Trondheim. We were not sure that we would be able to beat the bureaucracy and get all the documents necessary for the visa and her travel. We arrived in Trondheim in August 28th, and I started working on September 1st.
• I had the opportunity to attend RecSys 2015 with departmental funding, even though I had literally just arrived at the department. This helped me a lot in gaining momentum in the writing of the initial research proposal, submitted to the faculty and now already accepted. During RecSys, I had the chance to analyze different research challenges and had some ideas that I am looking forward to try some of this ideas.
• I ran a self-study seminar series titled Approximate and Scalable Inference for Complex Probabilistic Models in Recommender Systems. It consisted on the study of probabilistic models and inference algorithms. It included Probabilistic Matrix Factorization, Poisson Matrix Factorization, Bayesian variants of those models, models with side information (social network, trust network, kernelized models), latent gaussian models, and dynamics models (State-space, tensor-factorization, based on time dependent Dirichlet process). We also studied variational inference and plan to study INLA (Integrated Nested Laplace Approximation) and sampling methods (MCMC, HMC, Gibbs sampling) applied to matrix factorization probabilistic models.
• Last, but not least, we had our first child, Samir, born in Oct. 23 here in Trondheim.

I am really looking forward to this new year, 2015 was a great year with many changes. I hope to consolidate many open threads of research in 2016, and continue to live a happy live with my family.

## And so on and so forth!

Hello world!

Let’s get this thing started.

First of all, I’m not used to blogging in English, but I’m really excited to this new experience. This blog is intended to be a somehow personal, but also kind of professional accounting of my journey throughout a solid education in Computer Science research.

I should have started it sooner, probably in February 2012, when I first began my master course at School of Electrical and Computer Engineering, State University of Campinas (FEEC-Unicamp), but better late than never.

By now I am almost done with the 16 mandatory credits and I’ll concentrate mainly in the research project, which has gone through some change during the year. Later I’ll write about my research project and the courses that I’ve taken.

Looking back to it, I can say that it has been a mindful experience and I’ve been working with amazing and inspiring people and I hope my research results to be a screen of all the great things I’ve learned, experienced and thought.

So join me and I hope my writing will be as pleasant to you as all of this has been to me.