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.

# Tag: research review

## 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]**.