47,784 research outputs found
SibRank: Signed Bipartite Network Analysis for Neighbor-based Collaborative Ranking
Collaborative ranking is an emerging field of recommender systems that
utilizes users' preference data rather than rating values. Unfortunately,
neighbor-based collaborative ranking has gained little attention despite its
more flexibility and justifiability. This paper proposes a novel framework,
called SibRank that seeks to improve the state of the art neighbor-based
collaborative ranking methods. SibRank represents users' preferences as a
signed bipartite network, and finds similar users, through a novel personalized
ranking algorithm in signed networks
Visually-Aware Fashion Recommendation and Design with Generative Image Models
Building effective recommender systems for domains like fashion is
challenging due to the high level of subjectivity and the semantic complexity
of the features involved (i.e., fashion styles). Recent work has shown that
approaches to `visual' recommendation (e.g.~clothing, art, etc.) can be made
more accurate by incorporating visual signals directly into the recommendation
objective, using `off-the-shelf' feature representations derived from deep
networks. Here, we seek to extend this contribution by showing that
recommendation performance can be significantly improved by learning `fashion
aware' image representations directly, i.e., by training the image
representation (from the pixel level) and the recommender system jointly; this
contribution is related to recent work using Siamese CNNs, though we are able
to show improvements over state-of-the-art recommendation techniques such as
BPR and variants that make use of pre-trained visual features. Furthermore, we
show that our model can be used \emph{generatively}, i.e., given a user and a
product category, we can generate new images (i.e., clothing items) that are
most consistent with their personal taste. This represents a first step towards
building systems that go beyond recommending existing items from a product
corpus, but which can be used to suggest styles and aid the design of new
products.Comment: 10 pages, 6 figures. Accepted by ICDM'17 as a long pape
DeepRec: An Open-source Toolkit for Deep Learning based Recommendation
Deep learning based recommender systems have been extensively explored in
recent years. However, the large number of models proposed each year poses a
big challenge for both researchers and practitioners in reproducing the results
for further comparisons. Although a portion of papers provides source code,
they adopted different programming languages or different deep learning
packages, which also raises the bar in grasping the ideas. To alleviate this
problem, we released the open source project: \textbf{DeepRec}. In this
toolkit, we have implemented a number of deep learning based recommendation
algorithms using Python and the widely used deep learning package - Tensorflow.
Three major recommendation scenarios: rating prediction, top-N recommendation
(item ranking) and sequential recommendation, were considered. Meanwhile,
DeepRec maintains good modularity and extensibility to easily incorporate new
models into the framework. It is distributed under the terms of the GNU General
Public License. The source code is available at github:
\url{https://github.com/cheungdaven/DeepRec}Comment: Accepted by IJCAI-2019 Demonstrations Trac
Personalized and situation-aware multimodal route recommendations: the FAVOUR algorithm
Route choice in multimodal networks shows a considerable variation between
different individuals as well as the current situational context.
Personalization of recommendation algorithms are already common in many areas,
e.g., online retail. However, most online routing applications still provide
shortest distance or shortest travel-time routes only, neglecting individual
preferences as well as the current situation. Both aspects are of particular
importance in a multimodal setting as attractivity of some transportation modes
such as biking crucially depends on personal characteristics and exogenous
factors like the weather. This paper introduces the FAVourite rOUte
Recommendation (FAVOUR) approach to provide personalized, situation-aware route
proposals based on three steps: first, at the initialization stage, the user
provides limited information (home location, work place, mobility options,
sociodemographics) used to select one out of a small number of initial
profiles. Second, based on this information, a stated preference survey is
designed in order to sharpen the profile. In this step a mass preference prior
is used to encode the prior knowledge on preferences from the class identified
in step one. And third, subsequently the profile is continuously updated during
usage of the routing services. The last two steps use Bayesian learning
techniques in order to incorporate information from all contributing
individuals. The FAVOUR approach is presented in detail and tested on a small
number of survey participants. The experimental results on this real-world
dataset show that FAVOUR generates better-quality recommendations w.r.t.
alternative learning algorithms from the literature. In particular the
definition of the mass preference prior for initialization of step two is shown
to provide better predictions than a number of alternatives from the
literature.Comment: 12 pages, 6 figures, 1 table. Submitted to IEEE Transactions on
Intelligent Transportation Systems journal for publicatio
TribeFlow: Mining & Predicting User Trajectories
Which song will Smith listen to next? Which restaurant will Alice go to
tomorrow? Which product will John click next? These applications have in common
the prediction of user trajectories that are in a constant state of flux over a
hidden network (e.g. website links, geographic location). What users are doing
now may be unrelated to what they will be doing in an hour from now. Mindful of
these challenges we propose TribeFlow, a method designed to cope with the
complex challenges of learning personalized predictive models of
non-stationary, transient, and time-heterogeneous user trajectories. TribeFlow
is a general method that can perform next product recommendation, next song
recommendation, next location prediction, and general arbitrary-length user
trajectory prediction without domain-specific knowledge. TribeFlow is more
accurate and up to 413x faster than top competitors.Comment: To Appear at WWW 201
Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks
Recommender systems help users deal with information overload by providing
tailored item suggestions to them. The recommendation of news is often
considered to be challenging, since the relevance of an article for a user can
depend on a variety of factors, including the user's short-term reading
interests, the reader's context, or the recency or popularity of an article.
Previous work has shown that the use of Recurrent Neural Networks is promising
for the next-in-session prediction task, but has certain limitations when only
recorded item click sequences are used as input. In this work, we present a
contextual hybrid, deep learning based approach for session-based news
recommendation that is able to leverage a variety of information types. We
evaluated our approach on two public datasets, using a temporal evaluation
protocol that simulates the dynamics of a news portal in a realistic way. Our
results confirm the benefits of considering additional types of information,
including article popularity and recency, in the proposed way, resulting in
significantly higher recommendation accuracy and catalog coverage than other
session-based algorithms. Additional experiments show that the proposed
parameterizable loss function used in our method also allows us to balance two
usually conflicting quality factors, accuracy and novelty.
Keywords: Artificial Neural Networks, Context-Aware Recommender Systems,
Hybrid Recommender Systems, News Recommender Systems, Session-based
RecommendationComment: 20 pgs. Published at IEEE Access, Volume 7, 2019.
https://ieeexplore.ieee.org/document/890868
Choose Your Own Question: Encouraging Self-Personalization in Learning Path Construction
Learning Path Recommendation is the heart of adaptive learning, the
educational paradigm of an Interactive Educational System (IES) providing a
personalized learning experience based on the student's history of learning
activities. In typical existing IESs, the student must fully consume a
recommended learning item to be provided a new recommendation. This workflow
comes with several limitations. For example, there is no opportunity for the
student to give feedback on the choice of learning items made by the IES.
Furthermore, the mechanism by which the choice is made is opaque to the
student, limiting the student's ability to track their learning. To this end,
we introduce Rocket, a Tinder-like User Interface for a general class of IESs.
Rocket provides a visual representation of Artificial Intelligence
(AI)-extracted features of learning materials, allowing the student to quickly
decide whether the material meets their needs. The student can choose between
engaging with the material and receiving a new recommendation by swiping or
tapping. Rocket offers the following potential improvements for IES User
Interfaces: First, Rocket enhances the explainability of IES recommendations by
showing students a visual summary of the meaningful AI-extracted features used
in the decision-making process. Second, Rocket enables self-personalization of
the learning experience by leveraging the students' knowledge of their own
abilities and needs. Finally, Rocket provides students with fine-grained
information on their learning path, giving them an avenue to assess their own
skills and track their learning progress. We present the source code of Rocket,
in which we emphasize the independence and extensibility of each component, and
make it publicly available for all purposes
Heterogeneous Information Network Embedding for Recommendation
Due to the flexibility in modelling data heterogeneity, heterogeneous
information network (HIN) has been adopted to characterize complex and
heterogeneous auxiliary data in recommender systems, called HIN based
recommendation. It is challenging to develop effective methods for HIN based
recommendation in both extraction and exploitation of the information from
HINs. Most of HIN based recommendation methods rely on path based similarity,
which cannot fully mine latent structure features of users and items. In this
paper, we propose a novel heterogeneous network embedding based approach for
HIN based recommendation, called HERec. To embed HINs, we design a meta-path
based random walk strategy to generate meaningful node sequences for network
embedding. The learned node embeddings are first transformed by a set of fusion
functions, and subsequently integrated into an extended matrix factorization
(MF) model. The extended MF model together with fusion functions are jointly
optimized for the rating prediction task. Extensive experiments on three
real-world datasets demonstrate the effectiveness of the HERec model. Moreover,
we show the capability of the HERec model for the cold-start problem, and
reveal that the transformed embedding information from HINs can improve the
recommendation performance
Graph-based Collaborative Ranking
Data sparsity, that is a common problem in neighbor-based collaborative
filtering domain, usually complicates the process of item recommendation. This
problem is more serious in collaborative ranking domain, in which calculating
the users similarities and recommending items are based on ranking data. Some
graph-based approaches have been proposed to address the data sparsity problem,
but they suffer from two flaws. First, they fail to correctly model the users
priorities, and second, they cannot be used when the only available data is a
set of ranking instead of rating values. In this paper, we propose a novel
graph-based approach, called GRank, that is designed for collaborative ranking
domain. GRank can correctly model users priorities in a new tripartite graph
structure, and analyze it to directly infer a recommendation list. The
experimental results show a significant improvement in recommendation quality
compared to the state of the art graph-based recommendation algorithms and
other collaborative ranking techniques
DimensionRank: Personal Neural Representations for Personalized General Search
Web Search and Social Media have always been two of the most important
applications on the internet. We begin by giving a unified framework, called
general search, of which which all search and social media products can be seen
as instances.
DimensionRank is our main contribution. This is an algorithm for personalized
general search, based on neural networks. DimensionRank's bold innovation is to
model and represent each user using their own unique personal neural
representation vector, a learned representation in a real-valued
multidimensional vector space. This is the first internet service we are aware
of that to model each user with their own independent representation vector.
This is also the first service we are aware of to attempt personalization for
general web search. Also, neural representations allows us to present the first
Reddit-style algorithm, that is immune to the problem of "brigading". We
believe personalized general search will yield a search product orders of
magnitude better than Google's one-size-fits-all web search algorithm.
Finally, we announce Deep Revelations, a new search and social network
internet application based on DimensionRank
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