13,074 research outputs found
Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning
Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In
these media, dynamic and still elements are juxtaposed to create an artistic
and narrative experience. Creating a high-quality, aesthetically pleasing
cinemagraph requires isolating objects in a semantically meaningful way and
then selecting good start times and looping periods for those objects to
minimize visual artifacts (such a tearing). To achieve this, we present a new
technique that uses object recognition and semantic segmentation as part of an
optimization method to automatically create cinemagraphs from videos that are
both visually appealing and semantically meaningful. Given a scene with
multiple objects, there are many cinemagraphs one could create. Our method
evaluates these multiple candidates and presents the best one, as determined by
a model trained to predict human preferences in a collaborative way. We
demonstrate the effectiveness of our approach with multiple results and a user
study.Comment: To appear in ICCV 2017. Total 17 pages including the supplementary
materia
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders
Collaborative filtering (CF) has been successfully used to provide users with
personalized products and services. However, dealing with the increasing
sparseness of user-item matrix still remains a challenge. To tackle such issue,
hybrid CF such as combining with content based filtering and leveraging side
information of users and items has been extensively studied to enhance
performance. However, most of these approaches depend on hand-crafted feature
engineering, which are usually noise-prone and biased by different feature
extraction and selection schemes. In this paper, we propose a new hybrid model
by generalizing contractive auto-encoder paradigm into matrix factorization
framework with good scalability and computational efficiency, which jointly
model content information as representations of effectiveness and compactness,
and leverage implicit user feedback to make accurate recommendations. Extensive
experiments conducted over three large scale real datasets indicate the
proposed approach outperforms the compared methods for item recommendation.Comment: 4 pages, 3 figure
Discrete Factorization Machines for Fast Feature-based Recommendation
User and item features of side information are crucial for accurate
recommendation. However, the large number of feature dimensions, e.g., usually
larger than 10^7, results in expensive storage and computational cost. This
prohibits fast recommendation especially on mobile applications where the
computational resource is very limited. In this paper, we develop a generic
feature-based recommendation model, called Discrete Factorization Machine
(DFM), for fast and accurate recommendation. DFM binarizes the real-valued
model parameters (e.g., float32) of every feature embedding into binary codes
(e.g., boolean), and thus supports efficient storage and fast user-item score
computation. To avoid the severe quantization loss of the binarization, we
propose a convergent updating rule that resolves the challenging discrete
optimization of DFM. Through extensive experiments on two real-world datasets,
we show that 1) DFM consistently outperforms state-of-the-art binarized
recommendation models, and 2) DFM shows very competitive performance compared
to its real-valued version (FM), demonstrating the minimized quantization loss.
This work is accepted by IJCAI 2018.Comment: Appeared in IJCAI 201
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