564 research outputs found
Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation
The 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 we show how to generalize it to kernels of the dot product family preserving the efficiency. We also investigate on the elements which influence the sparsity of a standard cosine kernel. This analysis shows that the sparsity of the kernel strongly depends on the properties of the dataset, in particular on the long tail distribution. We compare our method with state-of-the-art algorithms achieving good results both in terms of efficiency and effectiveness
Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation
The 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 we show how to generalize it to kernels of the dot product family preserving the efficiency. We also investigate on the elements which influence the sparsity of a standard cosine kernel. This analysis shows that the sparsity of the kernel strongly depends on the properties of the dataset, in particular on the long tail distribution. We compare our method with state-of-the-art algorithms achieving good results both in terms of efficiency and effectiveness
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
LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates
State-of-the-art item recommendation algorithms, which apply
Factorization Machines (FM) as a scoring function and
pairwise ranking loss as a trainer (PRFM for short), have
been recently investigated for the implicit feedback based
context-aware recommendation problem (IFCAR). However,
good recommenders particularly emphasize on the accuracy
near the top of the ranked list, and typical pairwise loss functions
might not match well with such a requirement. In this
paper, we demonstrate, both theoretically and empirically,
PRFM models usually lead to non-optimal item recommendation
results due to such a mismatch. Inspired by the success
of LambdaRank, we introduce Lambda Factorization
Machines (LambdaFM), which is particularly intended for
optimizing ranking performance for IFCAR. We also point
out that the original lambda function suffers from the issue
of expensive computational complexity in such settings due
to a large amount of unobserved feedback. Hence, instead
of directly adopting the original lambda strategy, we create
three effective lambda surrogates by conducting a theoretical
analysis for lambda from the top-N optimization perspective.
Further, we prove that the proposed lambda surrogates
are generic and applicable to a large set of pairwise
ranking loss functions. Experimental results demonstrate
LambdaFM significantly outperforms state-of-the-art algorithms
on three real-world datasets in terms of four standard
ranking measures
A Batch Learning Framework for Scalable Personalized Ranking
In designing personalized ranking algorithms, it is desirable to encourage a
high precision at the top of the ranked list. Existing methods either seek a
smooth convex surrogate for a non-smooth ranking metric or directly modify
updating procedures to encourage top accuracy. In this work we point out that
these methods do not scale well to a large-scale setting, and this is partly
due to the inaccurate pointwise or pairwise rank estimation. We propose a new
framework for personalized ranking. It uses batch-based rank estimators and
smooth rank-sensitive loss functions. This new batch learning framework leads
to more stable and accurate rank approximations compared to previous work.
Moreover, it enables explicit use of parallel computation to speed up training.
We conduct empirical evaluation on three item recommendation tasks. Our method
shows consistent accuracy improvements over state-of-the-art methods.
Additionally, we observe time efficiency advantages when data scale increases.Comment: AAAI 2018, Feb 2-7, New Orleans, US
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