About
33
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Introduction
I work in the fields of recommender systems, information filtering, and machine learning. My current research mainly focuses on revealing and alleviating challenges of popularity bias and other potential bias issues in recommender systems.
Current institution
Additional affiliations
September 2013 - October 2019
Publications
Publications (33)
Group recommender systems aim to suggest appropriate products/services to a group of users rather than individuals. These recommendations rely solely on determining group preferences, which is accomplished by an aggregation technique that combines individuals' preferences. A plethora of aggregation techniques of various types have been developed so...
Collaborative filtering is specialized in suggesting appropriate products and services to the users concerning personal characteristics and past preferences without requiring any effort of users. It might be more efficient to collect preferences of users based on multiple sub-criteria of products and services. For this purpose, researchers propose...
The main goal of group recommender systems is to provide appropriate products/services for members of a group. Various aggregation techniques have been proposed for combining individual preferences and estimating recommendations based on counts of ratings, rankings, deviation, and rating averages. Also, the size of the groups and the recommendation...
Bu çalışmada, öneri sistemlerinin doğruluğunu artırmayı amaçlayan tür tabanlı bir yaklaşım ile grafik tabanlı yöntemi birleştiren hibrit bir model geliştirilmiştir. Geliştirilen model, kullanıcılara daha kişiselleştirilmiş ve isabetli tahminler sunmayı hedeflemektedir. İçerik tabanlı ve işbirlikçi filtreleme yöntemlerini ağırlıklandırılmış bir yapı...
Bu çalışmada, öneri sistemlerinde kullanılan kütüphanelerin, kullanıcılara özel olarak üretilen öneri listeleri üzerindeki etkileri ve bu kütüphanelere olan bağımlılığın oluşturabileceği sonuçlar incelenmiştir. Araştırmada, daha kişiselleştirilmiş ve başarılı öneriler üretebilen öneri kütüphanelerinin belirlenmesine yönelik çeşitli metrikler kullan...
ZET Son yıllarda internet üzerindeki veri miktarındaki hızlı artış, bilgi işleme sorunlarını çözmek amacıyla yapay zekâ tabanlı öneri sistemlerinin geliştirilmesine yol açmıştır. Bu sistemler, kullanıcıların ilgisini çekebilecek ürünleri sıralı listeler halinde önererek kullanıcı deneyimini iyileştirmeyi hedeflemektedir. Ancak, öneri sistemlerinde...
ZET Son yıllarda internet üzerindeki veri miktarındaki hızlı artış, bilgi işleme sorunlarını çözmek amacıyla yapay zekâ tabanlı öneri sistemlerinin geliştirilmesine yol açmıştır. Bu sistemler, kullanıcıların ilgisini çekebilecek ürünleri sıralı listeler halinde önererek kullanıcı deneyimini iyileştirmeyi hedeflemektedir. Ancak, öneri sistemlerinde...
Recommender systems aid users in discovering items of interest across various domains. However, these systems often suffer from popularity bias, disproportionately recommending popular items and neglecting less popular ones that may still appeal to users. We propose an efficient pre-processing technique to mitigate popularity bias in personality-aw...
The effective segmentation of brain tumors in Magnetic Resonance Images is a pivotal concern in neuro-oncology, serving as the foundation for accurate diagnosis, precise treatment planning, and diligent monitoring of disease progression. Precise segmentation is essential for determining the tumor's size, location, and potential growth, which is cru...
Purpose
Reading habit plays a pivotal role in individuals' personal and academic growth, making it essential to encourage among campus users. University libraries serve as valuable platforms to promote reading by providing access to a diverse range of books and resources. Recommending books through personalized systems not only helps campus users d...
Bu çalışmada, kuantum kuyu yapısındaki elektronların enerji seviyelerini etkileyen termal parametre ve buna karşılık gelen alt parametrelerim korelasyon ve regresyon analizi yapılmıştır. Yapılan bu analizle, ana ve alt parametrelerin kuantum kuyularında hapsolmuş elektronların optik ve elektronik özelliklerini nasıl değiştirdiği öngörülebilmektedir...
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Memory-based collaborative filtering schemes are among the most effective recommendation technologies in terms of prediction quality, despite commonly facing issues related to accuracy, scalability, and privacy. A prominent approach suggests an intuitively reasonable modification to the similarity function, which has been proven to provide more acc...
Recommender systems have become increasingly important in today’s digital age, but they are not without their challenges. One of the most significant challenges is that users are not always willing to share their preferences due to privacy concerns, yet they still require decent recommendations. Privacy-preserving collaborative recommenders remedy...
Recommender systems are emerging techniques guiding individuals with provided referrals by considering their past rating behaviors. By collecting multi-criteria preferences concentrating on distinguishing perspectives of the items, a new extension of traditional recommenders, multi-criteria recommender systems reveal how much a user likes an item a...
Recommender systems are subject to well‐known popularity bias issues, that is, they expose frequently rated items more in recommendation lists than less‐rated ones. Such a problem could also have varying effects on users with different gender, age, or rating behavior, which significantly diminishes the users' overall satisfaction with recommendatio...
It is known that collaborative filtering recommendation algorithms are usually biased towards some particular items (e.g., popular) in their produced ranked lists. In this study, we evaluate this problem from the perspective of blockbuster items that are both popular and highly-rated items. To this end, we first adopt an efficient method describing...
One of the main concerns related to personalized recommendations in recent years is how fair the provided referrals are for individuals differentiating in terms of particular features. In this study, we consider a critical protected attribute related to individuals, i.e., gender, and aim to analyze how the most prominent recommenders show varying p...
An efficient approach to handling the well-known popularity bias in recommendations is treating this issue by considering users' actual propensities on item popularity. This way, the recommender systems can also provide more calibrated individual recommendations. However, when estimating users' tendency on popularity, the existing methods consider...
Smart homes are equipped with easy-to-interact interfaces, providing a more comfortable living environment and less energy consumption. There are currently satisfactory approaches proposed to deliver adequate comfort and ease to smart home inhabitants through infrared sensors, motion sensors, and other similar technologies. However, the goal of red...
The popularity bias problem is one of the most prominent challenges of recommender systems, i.e., while a few heavily rated items receive much attention in presented recommendation lists, less popular ones are underrepresented even if they would be of close interest to the user. This structural tendency of recommendation algorithms causes several u...
Popularity bias is defined as the intrinsic tendency of recommendation algorithms to feature popular items more than unpopular ones in the ranked lists lists they produced. When investigating the adverse effects of popularity bias, the literature has usually focused on the most frequently rated items only. However, an item’s popularity does not alw...
Collaborative filtering recommendation algorithms are vulnerable against the popularity bias, including the most popular items repeatedly into the produced ranked lists. However, the research on popularity bias focuses solely on the number of times items are rated rather than the magnitude of the provided ratings when scrutinizing the adverse effec...
Collaborative filtering algorithms are efficient tools for providing recommendations with reasonable accuracy performances to individuals. However, the previous research has realized that these algorithms propagate an undesirable bias in favor of blockbuster items in their recommendations, resulting in recommendation lists dominated by such items....
Producing recommendations where tail items have been included more is usually considered one crucial task of recommender systems to improve the diversification ability of the platform. The final recommendations are generated using only a single algorithm in traditional settings; however, algorithms can show varying performances for users differenti...
Collaborative filtering algorithms are efficient tools for providing recommendations with reasonable accuracy performances to individuals. However, the previous research has realized that these algorithms are undesirably biased towards blockbuster items. i.e., both popular and highly-liked items, in their recommendations, resulting in recommendatio...
The exponential increase in energy demands continuously causes high price energy tariffs for domestic and commercial consumers. To overcome this problem, researchers strive to discover effective ways to reduce peak-hour energy demand through off-peak scheduling yielding low price energy tariffs. Efficient off-peak scheduling requires precise applia...
Collaborative filtering algorithms unwittingly produce ranked lists where a few popular items are recommended too frequently while the remaining vast amount of items get not deserved attention, also referred to as the popularity bias problem. Nevertheless, when investigating popularity bias issues in recommendations, the literature commonly estimat...
It is known that collaborative filtering recommendation algorithms are usually biased towards some particular items (e.g., popular) in their produced ranked lists. In this study, we evaluate this problem from the perspective of blockbuster items that are both popular and highly-rated items. To this end, we first introduce an efficient method descri...
Popularity bias is an undesirable phenomenon associated with recommendation algorithms where popular items tend to be suggested over long-tail ones, even if the latter would be of reasonable interest for individuals. Such intrinsic tendencies of the recommenders may lead to producing ranked lists, in which items are not equally covered along the po...
Group recommender systems are specialized in suggesting preferable products or services to a group of users rather than an individual by aggregating personal preferences of group members. In such expert systems, the initial task is to identify groups of similar users via clustering approaches as user groups are usually not predefined. However, clus...
Öneri sistemleri, bireysel kullanıcılara herhangi bir kişisel çaba gerektirmeden geçmişteki tercihlerine ve özelliklerine göre uygun ürünleri/hizmetleri öneren otomatikleştirilmiş araçlardır. Bu sistemlerde, işbirlikçi filtreleme algoritmaları, ürünler için bireysel tahminler veya kullanıcılar için tercih edilir ürünlerin sıralı bir listesini üretm...
The main goal of a group recommender system is to provide appropriate referrals to a group of users sharing common interests rather than individuals. Such group referrals are commonly produced by utilizing aggregation techniques that analyze the propensities of the whole group by combining the preferences of the users in the group. Although there e...
