4,515 research outputs found
IceBreaker: Solving Cold Start Problem for Video Recommendation Engines
Internet has brought about a tremendous increase in content of all forms and,
in that, video content constitutes the major backbone of the total content
being published as well as watched. Thus it becomes imperative for video
recommendation engines such as Hulu to look for novel and innovative ways to
recommend the newly added videos to their users. However, the problem with new
videos is that they lack any sort of metadata and user interaction so as to be
able to rate the videos for the consumers. To this effect, this paper
introduces the several techniques we develop for the Content Based Video
Relevance Prediction (CBVRP) Challenge being hosted by Hulu for the ACM
Multimedia Conference 2018. We employ different architectures on the CBVRP
dataset to make use of the provided frame and video level features and generate
predictions of videos that are similar to the other videos. We also implement
several ensemble strategies to explore complementarity between both the types
of provided features. The obtained results are encouraging and will impel the
boundaries of research for multimedia based video recommendation systems
Solving the Cold-Start Problem in Recommender Systems with Social Tags
In this paper, based on the user-tag-object tripartite graphs, we propose a
recommendation algorithm, which considers social tags as an important role for
information retrieval. Besides its low cost of computational time, the
experiment results of two real-world data sets, \emph{Del.icio.us} and
\emph{MovieLens}, show it can enhance the algorithmic accuracy and diversity.
Especially, it can obtain more personalized recommendation results when users
have diverse topics of tags. In addition, the numerical results on the
dependence of algorithmic accuracy indicates that the proposed algorithm is
particularly effective for small degree objects, which reminds us of the
well-known \emph{cold-start} problem in recommender systems. Further empirical
study shows that the proposed algorithm can significantly solve this problem in
social tagging systems with heterogeneous object degree distributions
CRUC: Cold-start Recommendations Using Collaborative Filtering in Internet of Things
The Internet of Things (IoT) aims at interconnecting everyday objects
(including both things and users) and then using this connection information to
provide customized user services. However, IoT does not work in its initial
stages without adequate acquisition of user preferences. This is caused by
cold-start problem that is a situation where only few users are interconnected.
To this end, we propose CRUC scheme - Cold-start Recommendations Using
Collaborative Filtering in IoT, involving formulation, filtering and prediction
steps. Extensive experiments over real cases and simulation have been performed
to evaluate the performance of CRUC scheme. Experimental results show that CRUC
efficiently solves the cold-start problem in IoT.Comment: Elsevier ESEP 2011: 9-10 December 2011, Singapore, Elsevier Energy
Procedia, http://www.elsevier.com/locate/procedia/, 201
A Probabilistic Model for the Cold-Start Problem in Rating Prediction using Click Data
One of the most efficient methods in collaborative filtering is matrix
factorization, which finds the latent vector representations of users and items
based on the ratings of users to items. However, a matrix factorization based
algorithm suffers from the cold-start problem: it cannot find latent vectors
for items to which previous ratings are not available. This paper utilizes
click data, which can be collected in abundance, to address the cold-start
problem. We propose a probabilistic item embedding model that learns item
representations from click data, and a model named EMB-MF, that connects it
with a probabilistic matrix factorization for rating prediction. The
experiments on three real-world datasets demonstrate that the proposed model is
not only effective in recommending items with no previous ratings, but also
outperforms competing methods, especially when the data is very sparse.Comment: ICONIP 201
Bandits Warm-up Cold Recommender Systems
We address the cold start problem in recommendation systems assuming no
contextual information is available neither about users, nor items. We consider
the case in which we only have access to a set of ratings of items by users.
Most of the existing works consider a batch setting, and use cross-validation
to tune parameters. The classical method consists in minimizing the root mean
square error over a training subset of the ratings which provides a
factorization of the matrix of ratings, interpreted as a latent representation
of items and users. Our contribution in this paper is 5-fold. First, we
explicit the issues raised by this kind of batch setting for users or items
with very few ratings. Then, we propose an online setting closer to the actual
use of recommender systems; this setting is inspired by the bandit framework.
The proposed methodology can be used to turn any recommender system dataset
(such as Netflix, MovieLens,...) into a sequential dataset. Then, we explicit a
strong and insightful link between contextual bandit algorithms and matrix
factorization; this leads us to a new algorithm that tackles the
exploration/exploitation dilemma associated to the cold start problem in a
strikingly new perspective. Finally, experimental evidence confirm that our
algorithm is effective in dealing with the cold start problem on publicly
available datasets. Overall, the goal of this paper is to bridge the gap
between recommender systems based on matrix factorizations and those based on
contextual bandits
Eliciting New Wikipedia Users' Interests via Automatically Mined Questionnaires: For a Warm Welcome, Not a Cold Start
Every day, thousands of users sign up as new Wikipedia contributors. Once
joined, these users have to decide which articles to contribute to, which users
to seek out and learn from or collaborate with, etc. Any such task is a hard
and potentially frustrating one given the sheer size of Wikipedia. Supporting
newcomers in their first steps by recommending articles they would enjoy
editing or editors they would enjoy collaborating with is thus a promising
route toward converting them into long-term contributors. Standard recommender
systems, however, rely on users' histories of previous interactions with the
platform. As such, these systems cannot make high-quality recommendations to
newcomers without any previous interactions -- the so-called cold-start
problem. The present paper addresses the cold-start problem on Wikipedia by
developing a method for automatically building short questionnaires that, when
completed by a newly registered Wikipedia user, can be used for a variety of
purposes, including article recommendations that can help new editors get
started. Our questionnaires are constructed based on the text of Wikipedia
articles as well as the history of contributions by the already onboarded
Wikipedia editors. We assess the quality of our questionnaire-based
recommendations in an offline evaluation using historical data, as well as an
online evaluation with hundreds of real Wikipedia newcomers, concluding that
our method provides cohesive, human-readable questions that perform well
against several baselines. By addressing the cold-start problem, this work can
help with the sustainable growth and maintenance of Wikipedia's diverse editor
community.Comment: Accepted at the 13th International AAAI Conference on Web and Social
Media (ICWSM-2019
SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images
Finding clothes that fit is a hot topic in the e-commerce fashion industry.
Most approaches addressing this problem are based on statistical methods
relying on historical data of articles purchased and returned to the store.
Such approaches suffer from the cold start problem for the thousands of
articles appearing on the shopping platforms every day, for which no prior
purchase history is available. We propose to employ visual data to infer size
and fit characteristics of fashion articles. We introduce SizeNet, a
weakly-supervised teacher-student training framework that leverages the power
of statistical models combined with the rich visual information from article
images to learn visual cues for size and fit characteristics, capable of
tackling the challenging cold start problem. Detailed experiments are performed
on thousands of textile garments, including dresses, trousers, knitwear, tops,
etc. from hundreds of different brands.Comment: IEEE Conference on Computer Vision and Pattern Recognition Workshop
(CVPRW) 2019 Focus on Fashion and Subjective Search - Understanding
Subjective Attributes of Data (FFSS-USAD
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