4,370 research outputs found
BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation
Feature-based matrix factorization techniques such as Factorization Machines (FM) have been proven to achieve impressive accuracy for the rating prediction task. However, most common recommendation scenarios are formulated as a top-N item ranking problem with implicit feedback (e.g., clicks, purchases)rather than explicit ratings. To address this problem, with both implicit feedback and feature information, we propose a feature-based collaborative boosting recommender called BoostFM, which integrates boosting into factorization models during the process of item ranking. Specifically, BoostFM is an adaptive boosting framework that linearly combines multiple homogeneous component recommenders, which are repeatedly constructed on the basis of the individual FM model by a re-weighting scheme. Two ways are proposed to efficiently train the component recommenders from the perspectives of both pairwise and listwise Learning-to-Rank (L2R). The properties of our proposed method are empirically studied on three real-world datasets. The experimental results show that BoostFM outperforms a number of state-of-the-art approaches for top-N recommendation
Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation
This paper is concerned with how to make efficient use of social information
to improve recommendations. Most existing social recommender systems assume
people share similar preferences with their social friends. Which, however, may
not hold true due to various motivations of making online friends and dynamics
of online social networks. Inspired by recent causal process based
recommendations that first model user exposures towards items and then use
these exposures to guide rating prediction, we utilize social information to
capture user exposures rather than user preferences. We assume that people get
information of products from their online friends and they do not have to share
similar preferences, which is less restrictive and seems closer to reality.
Under this new assumption, in this paper, we present a novel recommendation
approach (named SERec) to integrate social exposure into collaborative
filtering. We propose two methods to implement SERec, namely social
regularization and social boosting, each with different ways to construct
social exposures. Experiments on four real-world datasets demonstrate that our
methods outperform the state-of-the-art methods on top-N recommendations.
Further study compares the robustness and scalability of the two proposed
methods.Comment: Accepted for publication at the 32nd Conference on Artificial
Intelligence (AAAI 2018), New Orleans, Louisian
Can FCA-based Recommender System Suggest a Proper Classifier?
The paper briefly introduces multiple classifier systems and describes a new
algorithm, which improves classification accuracy by means of recommendation of
a proper algorithm to an object classification. This recommendation is done
assuming that a classifier is likely to predict the label of the object
correctly if it has correctly classified its neighbors. The process of
assigning a classifier to each object is based on Formal Concept Analysis. We
explain the idea of the algorithm with a toy example and describe our first
experiments with real-world datasets.Comment: 10 pages, 1 figure, 4 tables, ECAI 2014, workshop "What FCA can do
for "Artifficial Intelligence
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