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Item recommendation is the task of predicting a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common scenario with implicit feedback (e.g. clicks, purchases). There are many methods for item recommendation from implicit feedback like matrix factorization (MF) or adaptive knearest-neighbor… (More)

Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn the general taste of a user by factorizing the matrix over observed user-item preferences. On the other hand, MC methods model sequential behavior by learning a transition graph… (More)

<i>MyMediaLite</i> is a fast and scalable, multi-purpose library of recommender system algorithms, aimed both at recommender system researchers and practitioners. It addresses two common scenarios in collaborative filtering: <i>rating prediction</i> (e.g. on a scale of 1 to 5 stars) and <i>item prediction from positive-only implicit feedback</i> (e.g. from… (More)

The situation in which a choice is made is an important information for recommender systems. Context-aware recommenders take this information into account to make predictions. So far, the best performing method for context-aware rating prediction in terms of predictive accuracy is Multiverse Recommendation based on the Tucker tensor factorization model.… (More)

- Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme
- 2010 IEEE International Conference on Data Mining
- 2010

Cold-start scenarios in recommender systems are situations in which no prior events, like ratings or clicks, are known for certain users or items. To compute predictions in such cases, additional information about users (user attributes, e.g. gender, age, geographical location, occupation) and items (item attributes, e.g. genres, product categories,… (More)

- Steffen Rendle, Christoph Freudenthaler
- WSDM
- 2014

Pairwise algorithms are popular for learning recommender systems from implicit feedback. For each user, or more generally context, they try to discriminate between a small set of selected items and the large set of remaining (irrelevant) items. Learning is typically based on stochastic gradient descent (SGD) with uniformly drawn pairs. In this work, we show… (More)

A key element of the social networks on the internet such as Facebook and Flickr is that they encourage users to create connections between themselves, other users and objects.
One important task that has been approached in the literature that deals with such data is to use social graphs to predict user behavior (e.g. joining a group of interest). More… (More)

This work presents simple and fast structured Bayesian learning for matrix and tensor factorization models. An unblocked Gibbs sampler is proposed for factorization machines (FM) which are a general class of latent variable models subsuming matrix, tensor and many other factorization models. We empirically show on the large Netflix challenge dataset that… (More)

In this paper, we describe our approach to track 2 of the KDD Cup 2011. The task was to predict which 3 out of 6 candidate songs were positively rated – instead of not rated at all – by a user. The candidate items were not sampled uniformly, but according to their general popularity. We develop an adapted version of the Bayesian Personalized Ranking (BPR)… (More)

- Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Lars Schmidt-Thieme
- KDD Cup
- 2012

We develop an adapted version of the Bayesian Personalized Ranking (BPR) optimization criterion (Rendle et al., 2009) that takes the non-uniform sampling of negative test items — as in track 2 of the KDD Cup 2011 — into account. Furthermore, we present a modified version of the generic BPR learning algorithm that maximizes the new criterion. We use it to… (More)