70 research outputs found
Bandits Warm-up Cold Recommender Systems
We address the cold start problem in recommendation systems assuming no
contextual information is available neither about users, nor items. We consider
the case in which we only have access to a set of ratings of items by users.
Most of the existing works consider a batch setting, and use cross-validation
to tune parameters. The classical method consists in minimizing the root mean
square error over a training subset of the ratings which provides a
factorization of the matrix of ratings, interpreted as a latent representation
of items and users. Our contribution in this paper is 5-fold. First, we
explicit the issues raised by this kind of batch setting for users or items
with very few ratings. Then, we propose an online setting closer to the actual
use of recommender systems; this setting is inspired by the bandit framework.
The proposed methodology can be used to turn any recommender system dataset
(such as Netflix, MovieLens,...) into a sequential dataset. Then, we explicit a
strong and insightful link between contextual bandit algorithms and matrix
factorization; this leads us to a new algorithm that tackles the
exploration/exploitation dilemma associated to the cold start problem in a
strikingly new perspective. Finally, experimental evidence confirm that our
algorithm is effective in dealing with the cold start problem on publicly
available datasets. Overall, the goal of this paper is to bridge the gap
between recommender systems based on matrix factorizations and those based on
contextual bandits
Hybrid Collaborative Filtering with Autoencoders
Collaborative Filtering aims at exploiting the feedback of users to provide
personalised recommendations. Such algorithms look for latent variables in a
large sparse matrix of ratings. They can be enhanced by adding side information
to tackle the well-known cold start problem. While Neu-ral Networks have
tremendous success in image and speech recognition, they have received less
attention in Collaborative Filtering. This is all the more surprising that
Neural Networks are able to discover latent variables in large and
heterogeneous datasets. In this paper, we introduce a Collaborative Filtering
Neural network architecture aka CFN which computes a non-linear Matrix
Factorization from sparse rating inputs and side information. We show
experimentally on the MovieLens and Douban dataset that CFN outper-forms the
state of the art and benefits from side information. We provide an
implementation of the algorithm as a reusable plugin for Torch, a popular
Neural Network framework
AUC Optimisation and Collaborative Filtering
In recommendation systems, one is interested in the ranking of the predicted
items as opposed to other losses such as the mean squared error. Although a
variety of ways to evaluate rankings exist in the literature, here we focus on
the Area Under the ROC Curve (AUC) as it widely used and has a strong
theoretical underpinning. In practical recommendation, only items at the top of
the ranked list are presented to the users. With this in mind, we propose a
class of objective functions over matrix factorisations which primarily
represent a smooth surrogate for the real AUC, and in a special case we show
how to prioritise the top of the list. The objectives are differentiable and
optimised through a carefully designed stochastic gradient-descent-based
algorithm which scales linearly with the size of the data. In the special case
of square loss we show how to improve computational complexity by leveraging
previously computed measures. To understand theoretically the underlying matrix
factorisation approaches we study both the consistency of the loss functions
with respect to AUC, and generalisation using Rademacher theory. The resulting
generalisation analysis gives strong motivation for the optimisation under
study. Finally, we provide computation results as to the efficacy of the
proposed method using synthetic and real data
Unimodal Mono-Partite Matching in a Bandit Setting
We tackle a new emerging problem, which is finding an optimal monopartite
matching in a weighted graph. The semi-bandit version, where a full matching is
sampled at each iteration, has been addressed by \cite{ADMA}, creating an
algorithm with an expected regret matching
with players, iterations and a minimum reward gap . We reduce
this bound in two steps. First, as in \cite{GRAB} and \cite{UniRank} we use the
unimodality property of the expected reward on the appropriate graph to design
an algorithm with a regret in . Secondly, we show
that by moving the focus towards the main question `\emph{Is user better
than user ?}' this regret becomes
, where \Tilde{\Delta} > \Delta
derives from a better way of comparing users. Some experimental results finally
show these theoretical results are corroborated in practice
Online Matrix Completion Through Nuclear Norm Regularisation
Corrected a typo in the affiliationInternational audienceIt is the main goal of this paper to propose a novel method to perform matrix completion on-line. Motivated by a wide variety of applications, ranging from the design of recommender systems to sensor network localization through seismic data reconstruction, we consider the matrix completion problem when entries of the matrix of interest are observed gradually. Precisely, we place ourselves in the situation where the predictive rule should be refined incrementally, rather than recomputed from scratch each time the sample of observed entries increases. The extension of existing matrix completion methods to the sequential prediction context is indeed a major issue in the Big Data era, and yet little addressed in the literature. The algorithm promoted in this article builds upon the Soft Impute approach introduced in Mazumder et al. (2010). The major novelty essentially arises from the use of a randomised technique for both computing and updating the Singular Value Decomposition (SVD) involved in the algorithm. Though of disarming simplicity, the method proposed turns out to be very efficient, while requiring reduced computations. Several numerical experiments based on real datasets illustrating its performance are displayed, together with preliminary results giving it a theoretical basis
UniRank: Unimodal Bandit Algorithm for Online Ranking
We tackle a new emerging problem, which is finding an optimal monopartite
matching in a weighted graph. The semi-bandit version, where a full matching is
sampled at each iteration, has been addressed by \cite{ADMA}, creating an
algorithm with an expected regret matching
with players, iterations and a minimum reward gap . We reduce
this bound in two steps. First, as in \cite{GRAB} and \cite{UniRank} we use the
unimodality property of the expected reward on the appropriate graph to design
an algorithm with a regret in . Secondly, we show
that by moving the focus towards the main question `\emph{Is user better
than user ?}' this regret becomes
, where \Tilde{\Delta} > \Delta
derives from a better way of comparing users. Some experimental results finally
show these theoretical results are corroborated in practice
User Engagement as Evaluation: a Ranking or a Regression Problem?
1. Introduction2. Recsys Challenge 2014: Data and Protocol 2.1 Data Characteristics and Statistics 2.2 About User Engagement as Evaluation 2.3 Input Features for the Model3. Method 3.1 LambdaMART Model 3.2 Random Forests 3.3 Description of the Approach4. Experiments 4.1 Experimental results 4.2 Relevant Features5. Discussions6. Conclusions7. Acknowledgments8. ReferencesIn this paper, we describe the winning approach used on the RecSys Challenge 2014 which focuses on employing user en-gagement as evaluation of recommendations. On one hand, we regard the challenge as a ranking problem and apply the LambdaMART algorithm, which is a listwise model special-ized in a Learning To Rank approach. On the other hand, after noticing some specific characteristics of this challenge, we also consider it as a regression problem and use pointwise regression models such as Random Forests. We compare how these different methods can be modified or combined to improve the accuracy and robustness of our model and we draw the advantages or disadvantages of each approach
s-LIME: Reconciling Locality and Fidelity in Linear Explanations
The benefit of locality is one of the major premises of LIME, one of the most
prominent methods to explain black-box machine learning models. This emphasis
relies on the postulate that the more locally we look at the vicinity of an
instance, the simpler the black-box model becomes, and the more accurately we
can mimic it with a linear surrogate. As logical as this seems, our findings
suggest that, with the current design of LIME, the surrogate model may
degenerate when the explanation is too local, namely, when the bandwidth
parameter tends to zero. Based on this observation, the contribution
of this paper is twofold. Firstly, we study the impact of both the bandwidth
and the training vicinity on the fidelity and semantics of LIME explanations.
Secondly, and based on our findings, we propose \slime, an extension of LIME
that reconciles fidelity and locality
A Phase TRansition-Based Perspective on Multiple Instance Kernels
International audienceThis paper is concerned with Relational Support Vector Machines, at the intersection of Support Vector Machines (SVM) and Inductive Logic Programming or Relational Learning. The so-called phase transition framework, originally developed for constraint satisfaction problems, has been extended to relational learning and it has provided relevant insights into the limitations and difficulties hereof. The goal of this paper is to examine relational SVMs and specifically Multiple Instance (MI) Kernels along the phase transition framework. A relaxation of the MI-SVM problem formalized as a linear programming problem (LPP) is defined and we show that the LPP satisfiability rate induces a lower bound on the MI-SVM generalization error. An extensive experimental study shows the existence of a critical region, where both LPP unsatisfiability and MI-SVM error rates are high. An interpretation for these results is proposed
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