3,038 research outputs found
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
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
Apache Lucene as Content-Based-Filtering Recommender System: 3 Lessons Learned
For the past few years, we used Apache Lucene as recommendation frame-work in
our scholarly-literature recommender system of the reference-management
software Docear. In this paper, we share three lessons learned from our work
with Lucene. First, recommendations with relevance scores below 0.025 tend to
have significantly lower click-through rates than recommendations with
relevance scores above 0.025. Second, by picking ten recommendations randomly
from Lucene's top50 search results, click-through rate decreased by 15%,
compared to recommending the top10 results. Third, the number of returned
search results tend to predict how high click-through rates will be: when
Lucene returns less than 1,000 search results, click-through rates tend to be
around half as high as if 1,000+ results are returned.Comment: Accepted for publication at the 5th International Workshop on
Bibliometric-enhanced Information Retrieval (BIR2017
Patterns of Multistakeholder Recommendation
Recommender systems are personalized information systems. However, in many
settings, the end-user of the recommendations is not the only party whose needs
must be represented in recommendation generation. Incorporating this insight
gives rise to the notion of multistakeholder recommendation, in which the
interests of multiple parties are represented in recommendation algorithms and
evaluation. In this paper, we identify patterns of stakeholder utility that
characterize different multistakeholder recommendation applications, and
provide a taxonomy of the different possible systems, only some of which have
currently been implemented.Comment: Presented at the 2017 Workshop on Value-Aware and Multistakeholder
Recommendatio
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they
are ubiquitous in today's online environment, and effective at finding items
that meet user needs and tastes. As the reach of recommender systems has
extended, it has become apparent that the single-minded focus on the user
common to academic research has obscured other important aspects of
recommendation outcomes. Properties such as fairness, balance, profitability,
and reciprocity are not captured by typical metrics for recommender system
evaluation. The concept of multistakeholder recommendation has emerged as a
unifying framework for describing and understanding recommendation settings
where the end user is not the sole focus. This article describes the origins of
multistakeholder recommendation, and the landscape of system designs. It
provides illustrative examples of current research, as well as outlining open
questions and research directions for the field.Comment: 64 page
Collaborative Competitive filtering II: Optimal Recommendation and Collaborative Games
Recommender systems have emerged as a new weapon to help online firms to
realize many of their strategic goals (e.g., to improve sales, revenue,
customer experience etc.). However, many existing techniques commonly approach
these goals by seeking to recover preference (e.g., estimating ratings) in a
matrix completion framework. This paper aims to bridge this significant gap
between the clearly-defined strategic objectives and the not-so-well-justified
proxy.
We show it is advantageous to think of a recommender system as an analogy to
a monopoly economic market with the system as the sole seller, users as the
buyers and items as the goods. This new perspective motivates a game-theoretic
formulation for recommendation that enables us to identify the optimal
recommendation policy by explicit optimizing certain strategic goals. In this
paper, we revisit and extend our prior work, the Collaborative-Competitive
Filtering preference model, towards a game-theoretic framework. The proposed
framework consists of two components. First, a conditional preference model
that characterizes how a user would respond to a recommendation action; Second,
knowing in advance how the user would respond, how a recommender system should
act (i.e., recommend) strategically to maximize its goals. We show how
objectives such as click-through rate, sales revenue and consumption diversity
can be optimized explicitly in this framework. Experiments are conducted on a
commercial recommender system and demonstrate promising results.Comment: 10 pages, 5 figures; Recommender system, Collaborative filterin
Indian Regional Movie Dataset for Recommender Systems
Indian regional movie dataset is the first database of regional Indian
movies, users and their ratings. It consists of movies belonging to 18
different Indian regional languages and metadata of users with varying
demographics. Through this dataset, the diversity of Indian regional cinema and
its huge viewership is captured. We analyze the dataset that contains roughly
10K ratings of 919 users and 2,851 movies using some supervised and
unsupervised collaborative filtering techniques like Probabilistic Matrix
Factorization, Matrix Completion, Blind Compressed Sensing etc. The dataset
consists of metadata information of users like age, occupation, home state and
known languages. It also consists of metadata of movies like genre, language,
release year and cast. India has a wide base of viewers which is evident by the
large number of movies released every year and the huge box-office revenue.
This dataset can be used for designing recommendation systems for Indian users
and regional movies, which do not, yet, exist. The dataset can be downloaded
from \href{https://goo.gl/EmTPv6}{https://goo.gl/EmTPv6}.Comment: 7 pages, 8 figures, open-source Indian movie rating dataset, metadata
of movies and user
Diffusion-like recommendation with enhanced similarity of objects
In last decades, diversity and accuracy have been regarded as two important
measures in evaluating a recommendation model. However, a clear concern is that
a model focusing excessively on one measure will put the other one at risk,
thus it is not easy to greatly improve diversity and accuracy simultaneously.
In this paper, we propose to enhance the Resource-Allocation (RA) similarity in
resource transfer equations of diffusion-like models, by giving a tunable
exponent to the RA similarity, and traversing the value of the exponent to
achieve the optimal recommendation results. In this way, we can increase the
recommendation scores (allocated resource) of many unpopular objects.
Experiments on three benchmark data sets, MovieLens, Netflix, and RateYourMusic
show that the modified models can yield remarkable performance improvement
compared with the original ones
A Review on Recommendation Systems: Context-aware to Social-based
The number of Internet users had grown rapidly enticing companies and
cooperations to make full use of recommendation infrastructures. Consequently,
online advertisement companies emerged to aid us in the presence of numerous
items and users. Even as a user, you may find yourself drowned in a set of
items that you think you might need, but you are not sure if you should try
them. Those items could be online services, products, places or even a person
for a friendship. Therefore, we need recommender systems that pave the way and
help us making good decisions. This paper provides a review on traditional
recommendation systems, recommendation system evaluations and metrics,
context-aware recommendation systems, and social-based recommendation systems.
While it is hard to include all the information in a brief review paper, we try
to have an introductory review over the essentials of recommendation systems.
More detailed information on each chapter will be found in the corresponding
references. For the purpose of explaining the concept in a different way, we
provided slides available on
https://www.slideshare.net/MahdiSeyednejad/recommender-systems-97094937.Comment: 44 pages without bibliography, 4 chapters, Slide presentation:
https://www.slideshare.net/MahdiSeyednejad/recommender-systems-9709493
Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations
The trade-off between relevance and fairness in personalized recommendations
has been explored in recent works, with the goal of minimizing learned
discrimination towards certain demographics while still producing relevant
results.
We present a fairness-aware variation of the Maximal Marginal Relevance (MMR)
re-ranking method which uses representations of demographic groups computed
using a labeled dataset. This method is intended to incorporate fairness with
respect to these demographic groups.
We perform an experiment on a stock photo dataset and examine the trade-off
between relevance and fairness against a well known baseline, MMR, by using
human judgment to examine the results of the re-ranking when using different
fractions of a labeled dataset, and by performing a quantitative analysis on
the ranked results of a set of query images. We show that our proposed method
can incorporate fairness in the ranked results while obtaining higher precision
than the baseline, while our case study shows that even a limited amount of
labeled data can be used to compute the representations to obtain fairness.
This method can be used as a post-processing step for recommender systems and
search
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