Browsing Bilgisayar Mühendisliği Bölümü by Author "0000-0003-3818-6712"
Now showing items 1-13 of 13
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A novel classification‐based shilling attack detection approach for multi‐criteria recommender systems
Turkoglu Kaya, Tugba; Yalcin, Emre; Kaleli, Cihan (Wiley, Mayıs, 202)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 ... -
A survey of smart home energy conservation techniques
Fakhar, Muhemmed Zaman; Yalcin, Emre; Bilge, Alper (Elsevier, Mart, 2023)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 ... -
An entropy empowered hybridized aggregation technique for group recommender systems
Yalcin Emre; Ismailoglu Firat; Bilge Alper (Elsevier, 15.03.2021)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 ... -
Binary multicriteria collaborative filtering
Yalcin Emre; Bilge Alper (Scientific and technological research council of turkey, 30.11.2020)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 ... -
Evaluating unfairness of popularity bias in recommender systems: A comprehensive user-centric analysis
Yalcin, Emre; Bilge, Alper (Elsevier, 2022)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 ... -
Exploring potential biases towards blockbuster items in ranking-based recommendations
Yalcin Emre (Springer, 2022)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 ... -
IESR: Instant Energy Scheduling Recommendations for Cost Saving in Smart Homes
Fakhar, Muhammad Zaman; Yalçın, Emre; Bilge, Alper (IEEE, 10.05.2022)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 ... -
Investigating and counteracting popularity bias in group recommendations
Yalcin Emre; Bilge Alper (Elsevier, 2021)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 ... -
Novel automatic group identification approaches for group recommendation
Yalcin Emre; Bilge Alper (Elsevier, 2021)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 ... -
Popularity bias in personality perspective: An analysis of how personality traits expose individuals to the unfair recommendation
Yalcin, Emre; Bilge, Alper (Wiley, Şubat, 202)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 ... -
Robustness of privacy-preserving collaborative recommenders against popularity bias problem
Gulsoy, Mert; Yalcin, Emre; Bilge, Alper (PeerJ, Temmuz, 20)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 ... -
The Unfairness of Collaborative Filtering Algorithms’ Bias Towards Blockbuster Items
Yalcin, Emre (Ocak, 2023)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 ... -
Treating adverse effects of blockbuster bias on beyond-accuracy quality of personalized recommendations
Yalcin Emre; Bilge Alper (Elsevier, 2022)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 ...