1,414 research outputs found
Promoting cold-start items in recommender systems
As one of major challenges, cold-start problem plagues nearly all recommender
systems. In particular, new items will be overlooked, impeding the development
of new products online. Given limited resources, how to utilize the knowledge
of recommender systems and design efficient marketing strategy for new items is
extremely important. In this paper, we convert this ticklish issue into a clear
mathematical problem based on a bipartite network representation. Under the
most widely used algorithm in real e-commerce recommender systems, so-called
the item-based collaborative filtering, we show that to simply push new items
to active users is not a good strategy. To our surprise, experiments on real
recommender systems indicate that to connect new items with some less active
users will statistically yield better performance, namely these new items will
have more chance to appear in other users' recommendation lists. Further
analysis suggests that the disassortative nature of recommender systems
contributes to such observation. In a word, getting in-depth understanding on
recommender systems could pave the way for the owners to popularize their
cold-start products with low costs.Comment: 6 pages, 6 figure
A Faster Algorithm to Build New Users Similarity List in Neighbourhood-based Collaborative Filtering
Neighbourhood-based Collaborative Filtering (CF) has been applied in the
industry for several decades, because of the easy implementation and high
recommendation accuracy. As the core of neighbourhood-based CF, the task of
dynamically maintaining users' similarity list is challenged by cold-start
problem and scalability problem. Recently, several methods are presented on
solving the two problems. However, these methods applied an algorithm
to compute the similarity list in a special case, where the new users, with
enough recommendation data, have the same rating list. To address the problem
of large computational cost caused by the special case, we design a faster
() algorithm, TwinSearch Algorithm, to avoid computing and
sorting the similarity list for the new users repeatedly to save the
computational resources. Both theoretical and experimental results show that
the TwinSearch Algorithm achieves better running time than the traditional
method
A Survey on Fairness-aware Recommender Systems
As information filtering services, recommender systems have extremely
enriched our daily life by providing personalized suggestions and facilitating
people in decision-making, which makes them vital and indispensable to human
society in the information era. However, as people become more dependent on
them, recent studies show that recommender systems potentially own
unintentional impacts on society and individuals because of their unfairness
(e.g., gender discrimination in job recommendations). To develop trustworthy
services, it is crucial to devise fairness-aware recommender systems that can
mitigate these bias issues. In this survey, we summarise existing methodologies
and practices of fairness in recommender systems. Firstly, we present concepts
of fairness in different recommendation scenarios, comprehensively categorize
current advances, and introduce typical methods to promote fairness in
different stages of recommender systems. Next, after introducing datasets and
evaluation metrics applied to assess the fairness of recommender systems, we
will delve into the significant influence that fairness-aware recommender
systems exert on real-world industrial applications. Subsequently, we highlight
the connection between fairness and other principles of trustworthy recommender
systems, aiming to consider trustworthiness principles holistically while
advocating for fairness. Finally, we summarize this review, spotlighting
promising opportunities in comprehending concepts, frameworks, the balance
between accuracy and fairness, and the ties with trustworthiness, with the
ultimate goal of fostering the development of fairness-aware recommender
systems.Comment: 27 pages, 9 figure
Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start
E-commerce platforms provide their customers with ranked lists of recommended
items matching the customers' preferences. Merchants on e-commerce platforms
would like their items to appear as high as possible in the top-N of these
ranked lists. In this paper, we demonstrate how unscrupulous merchants can
create item images that artificially promote their products, improving their
rankings. Recommender systems that use images to address the cold start problem
are vulnerable to this security risk. We describe a new type of attack,
Adversarial Item Promotion (AIP), that strikes directly at the core of Top-N
recommenders: the ranking mechanism itself. Existing work on adversarial images
in recommender systems investigates the implications of conventional attacks,
which target deep learning classifiers. In contrast, our AIP attacks are
embedding attacks that seek to push features representations in a way that
fools the ranker (not a classifier) and directly lead to item promotion. We
introduce three AIP attacks insider attack, expert attack, and semantic attack,
which are defined with respect to three successively more realistic attack
models. Our experiments evaluate the danger of these attacks when mounted
against three representative visually-aware recommender algorithms in a
framework that uses images to address cold start. We also evaluate two common
defenses against adversarial images in the classification scenario and show
that these simple defenses do not eliminate the danger of AIP attacks. In sum,
we show that using images to address cold start opens recommender systems to
potential threats with clear practical implications. To facilitate future
research, we release an implementation of our attacks and defenses, which
allows reproduction and extension.Comment: Our code is available at https://github.com/liuzrcc/AI
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