869 research outputs found
Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario
Item cold-start is a classical issue in recommender systems that affects
anime and manga recommendations as well. This problem can be framed as follows:
how to predict whether a user will like a manga that received few ratings from
the community? Content-based techniques can alleviate this issue but require
extra information, that is usually expensive to gather. In this paper, we use a
deep learning technique, Illustration2Vec, to easily extract tag information
from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE
(Blended Alternate Least Squares with Explanation), a new model for
collaborative filtering, that benefits from this extra information to recommend
mangas. We show, using real data from an online manga recommender system called
Mangaki, that our model improves substantially the quality of recommendations,
especially for less-known manga, and is able to provide an interpretation of
the taste of the users.Comment: 6 pages, 3 figures, 1 table, accepted at the MANPU 2017 workshop,
co-located with ICDAR 2017 in Kyoto on November 10, 201
Statistical Significance of the Netflix Challenge
Inspired by the legacy of the Netflix contest, we provide an overview of what
has been learned---from our own efforts, and those of others---concerning the
problems of collaborative filtering and recommender systems. The data set
consists of about 100 million movie ratings (from 1 to 5 stars) involving some
480 thousand users and some 18 thousand movies; the associated ratings matrix
is about 99% sparse. The goal is to predict ratings that users will give to
movies; systems which can do this accurately have significant commercial
applications, particularly on the world wide web. We discuss, in some detail,
approaches to "baseline" modeling, singular value decomposition (SVD), as well
as kNN (nearest neighbor) and neural network models; temporal effects,
cross-validation issues, ensemble methods and other considerations are
discussed as well. We compare existing models in a search for new models, and
also discuss the mission-critical issues of penalization and parameter
shrinkage which arise when the dimensions of a parameter space reaches into the
millions. Although much work on such problems has been carried out by the
computer science and machine learning communities, our goal here is to address
a statistical audience, and to provide a primarily statistical treatment of the
lessons that have been learned from this remarkable set of data.Comment: Published in at http://dx.doi.org/10.1214/11-STS368 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
RecSys Challenge 2016: job recommendations based on preselection of offers and gradient boosting
We present the Mim-Solution's approach to the RecSys Challenge 2016, which
ranked 2nd. The goal of the competition was to prepare job recommendations for
the users of the website Xing.com.
Our two phase algorithm consists of candidate selection followed by the
candidate ranking. We ranked the candidates by the predicted probability that
the user will positively interact with the job offer. We have used Gradient
Boosting Decision Trees as the regression tool.Comment: 6 pages, 1 figure, 2 tables, Description of 2nd place winning
solution of RecSys 2016 Challange. To be published in RecSys'16 Challange
Proceeding
On hybrid modular recommendation systems for video streaming
The recommendation systems aim to improve the user engagement by recommending
appropriate personalized content to users, exploiting information about their
preferences. We propose the enabler, a hybrid recommendation system which
employs various machine-learning (ML) algorithms for learning an efficient
combination of several recommendation algorithms and selects the best blending
for a given input.Specifically, it integrates three layers, namely, the trainer
which trains the underlying recommenders, the blender which determines the most
efficient combination of the recommenders, and the tester for assessing the
performance of the system. The enabler incorporates a variety of recommendation
algorithms that span from collaborative filtering and content-based techniques
to ones based on neural networks. It uses the nested cross validation for
automatically selecting the best ML algorithm along with its hyper-parameter
values for the given input, according to a specific metric. The enabler can be
easily extended to include other recommenders and blenders. The enabler has
been extensively evaluated in the context of video-streaming. It outperforms
various other algorithms, when tested on the Movielens 1M benchmark
dataset.encouraging results. Moreover For example, it achieves an RMSE of
0.8206, compared to the state-of-the-art performance of the AutoRec and SVD,
0.827 and 0.845, respectively. A pilot web-based recommendation system was
developed and tested in the production environment of a large telecom operator
in Greece. Volunteer customers of the video-streaming service provided by the
telecom operator employed the system in the context of an out-in-the-wild field
study with a post-analysis of the enabler, using the collected ratings of the
pilot, demonstrated that it significantly outperforms several popular
recommendation algorithms
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