1,721,062 research outputs found
Transfer Learning by Kernel Meta-Learning
No full textA crucial issue in machine learning is how to learn appropriate representations for data. Recently, much work has been devoted to kernel learning, that is, the problem of finding a good kernel matrix for a given task. This can be done in a semi-supervised learning setting by using a large set of unlabeled data and a (typically small) set of i.i.d. labeled data. Another, even more challenging problem, is how one can exploit partially labeled data of a source task to learn good representations for a different, but related, target task. This is the main subject of transfer learning.
In this paper, we present a novel approach to transfer learning based on kernel learning. Specifically, we propose a kernel meta-learning algorithm which, starting from a basic kernel, tries to learn chains of kernel transforms that are able to produce good kernel matrices for the source tasks. The same sequence of transformations can be then applied to compute the kernel matrix for new related target tasks. We report on the application of this method to the five datasets of the Unsupervised and Transfer Learning (UTL) challenge benchmark1 , where we won the first phase of the competition
A Preliminary Study of a Recommender System for the Million Songs Dataset Challenge
No full textIn this paper, the preliminary study we have conducted on the Million Songs Dataset (MSD) challenge is described. The task of the competition was to suggest a set of songs to a user given half of its listening history and complete listening history of other 1 million people. We focus on memory-based collaborative filtering approaches since they are able to deal with large datasets in an efficient and effective way. In particular, we investigated on i) defining suitable similarity functions, ii) studying the effect of the “locality” of the collaborative scoring function, that is, how many of the neirest neighboors (and how much) they influence the score computation, and iii) aggregating multiple ranking strategies to define the overall recommendation. Using this technique we won the MSD challenge which counted about 150 registered teams
Convex AUC optimization for top-N recommendation with implicit feedback
No full textIn this paper, an effective collaborative filtering algorithm for top-N item recommendation with implicit feedback is proposed. The task of top-N item recommendation is to predict a ranking of items (movies, books, songs, or products in general) that can be of interest for a user based on earlier preferences of the user. We focus on implicit feedback where preferences are given in the form of binary events/ratings. Differently from state-of-the-art methods, the method proposed is designed to optimize the AUC directly within a margin maximization paradigm. Specifically, this turns out in a
simple constrained quadratic optimization problem, one fo
r each user. Experiments performed on several benchmarks show tha
t our method significantly outperforms state-of-the-art matrix
factorization methods in terms of AUC of the obtained predictions
An Efficient SMO-like Algorithm for Multiclass SVM
No full textStarting from a reformulation of Cramer & Singer Mul-
ticlass Kernel Machine, we propose a Sequential Minimal Opti-
mization (SMO) like algorithm for incremental and fast optimiza-
tion of the lagrangian. The proposed formulation allowed us to
dene very eective new pattern selection strategies which lead to
better empirical results
Interpretable Preference Learning: A Game Theoretic Framework for Large Margin On-Line Feature and Rule Learning
No full textA large body of research is currently investigating on the connection between machine learning and game theory. In this work, game theory notions are injected into a preference learning framework. Specifically, a preference learning problem is seen as a two-players zero-sum game. An algorithm is proposed to incrementally include new useful features into the hypothesis. This can be particularly important when dealing with a very large number of potential features like, for instance, in relational learning and rule extraction. A game theoretical analysis is used to demonstrate the convergence of the algorithm. Furthermore, leveraging on the natural analogy between features and rules, the resulting models can be easily interpreted by humans. An extensive set of experiments on classification tasks shows the effectiveness of the proposed method in terms of interpretability and feature selection quality, with accuracy at the state-of-the-art
Disjunctive Boolean Kernel based Collaborative Filtering for top-N item recommendation
No full textIn many real-world recommendation tasks the available data consists only of simple interactions between users and items, such as clicks and views, called implicit feedback. In this kind of scenarios model based pairwise methods have shown of being one of the most promising approaches. In this paper, we propose a principled and efficient kernel- based collaborative filtering method for top-N item recommendation inspired by pairwise preference learning. We also propose a new boolean kernel, called Monotone Disjunctive Kernel, which is able to alleviate the sparsity issue that is one of the main problem in collaborative filtering contexts. The embedding of this kernel is composed by all the combina-Tions of a certain degree d of the input variables, and these combined features are semantically interpreted as disjunctions of the input variables. Experiments on several CF datasets have shown the effectiveness and the efficiency of the proposed kernel-based method
Efficient top-n recommendation for very large scale binary rated datasets
No full textWe present a simple and scalable algorithm for top-N recommen-
dation able to deal with very large datasets and (binary rated) im-
plicit feedback. We focus on memory-based collaborative filtering
algorithms similar to the well known neighboor based technique
for explicit feedback. The major difference, that makes the algo-
rithm particularly scalable, is that it uses positive feedback only
and no explicit computation of the complete (user-by-user or item-
by-item) similarity matrix needs to be performed.
The study of the proposed algorithm has been conducted on data
from the Million Songs Dataset (MSD) challenge whose task was
to suggest a set of songs (out of more than 380k available songs)
to more than 100k users given half of the user listening history and
complete listening history of other 1 million people.
In particular, we investigate on the entire recommendation pipeline,
starting from the definition of suitable similarity and scoring func-
tions and suggestions on how to aggregate multiple ranking strate-
gies to define the overall recommendation. The technique we are
proposing extends and improves the one that already won the MSD
challenge last year
Kernel based collaborative filtering for very large scale top-N item recommendation
No full textThe increasing availability of implicit feedback datasets has raised the interest in developing effective collaborative filtering techniques able to deal asymmetrically with unambiguous positive feedback and ambiguous negative feedback. In this paper, we propose a principled kernel-based collaborative filtering method for top-N item recommendation with implicit feedback. We present an efficient implementation using the linear kernel, and how to generalize it to other kernels preserving efficiency. We compare our method with the state-of-the-art algorithm on the Million Songs Dataset achieving an execution about 5 time faster, while having comparable effectiveness
Multiclass Classification with Multi-Prototype Support Vector Machines
Full text linkWinner-take-all multiclass classifiers are built on the top of a set of prototypes each representing one of the available classes. A pattern is then classified with the label associated to the most 'similar' prototype. Recent proposal of SVM extensions to multiclass can be considered instances of the same strategy with one prototype per class.
The multi-prototype SVM proposed in this paper extends multiclass SVM to multiple prototypes per class. It allows to combine several vectors in a principled way to obtain large margin decision functions. For this problem, we give a compact constrained quadratic formulation and we propose a greedy optimization algorithm able to find locally optimal solutions for the non convex objective function.
This algorithm proceeds by reducing the overall problem into a series of simpler convex problems. For the solution of these reduced problems an efficient optimization algorithm is proposed. A number of pattern selection strategies are then discussed to speed-up the optimization process. In addition, given the combinatorial nature of the overall problem, stochastic search strategies are suggested to escape from local minima which are not globally optimal.
Finally, we report experiments on a number of datasets. The performance obtained using few simple linear prototypes is comparable to that obtained by state-of-the-art kernel-based methods but with a significant reduction (of one or two orders) in response time
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