Browsing by Author "Can, Ali Selcuk"

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Destination Image From the Perspective of Travel Intermediaries
(Routledge Journals, Taylor & Francis Ltd, 2010) Alaeddinoglu, Faruk; Can, Ali Selcuk
This paper primarily focuses on assessing the destination image of Turkey held by UK-based travel intermediaries such as tour operators and travel agencies. The strongest and weakest destination attributes of Turkey are explored in the paper through the structured methodology by applying primary research. Understanding the image of a destination helps not only to determine a promotional policy for her but also to influence tourists' decision making process. The research findings reveal that with regard to affective components of destination image items, Turkey is perceived as an arousing rather than a sleepy holiday destination. The main strengths of Turkey in terms of cognitive/perceptual destination components are cultural, historical, and natural attractions, hospitality of people, and appealing cuisine. On the other hand, the weakest destination attributes of Turkey are the lack and inadequacies of stability, safety, cleanliness of the environment, and local infrastructures. Furthermore, the country has a weakly good overall destination image from the UK-based travel intermediaries' perspectives. The findings of the research suggest that the affective, cognitive/perceptual, and overall destination image of Turkey does not differ between mass and specialist travel intermediaries.
Identification and Classification of Nature-Based Tourism Resources: Western Lake Van Basin, Turkey
(Elsevier Science Bv, 2011) Alaeddinoglu, Faruk; Can, Ali Selcuk
Today, nature-based tourism is one of the important export items of tourism industry in many countries such as Australia, Kenya, Nepal, and New Zealand. However, the nature-based tourism resources of Turkey cannot be promoted since they have not been identified and classified yet. The aim of this paper is to identify and assess the natural resources having tourism potential to be developed in the western part of Lake Van basin. The increasing environmental awareness among consumers has lead tourism managers and plannners to satisfy this type of tourists' needs by searching new tourism resources. First step for the effective planning is to systematically determine the resources and assess the values of them. The assessment criteria in this paper are attraction levels, infrastructure, level of environmental degradation, and accessibility. The 23 natural resources in the research area were classified based on Priskin's control list approach by applying several experts' opinions and making journey to the sites. The places of nature-based tourism attractions were determined with Global Positioning System and this information were evaluated in the Geographic Information System based program of Mapinfo and hundreds of pictures were taken from all perspectives in the research area. The findings of research revealed that the sites have middle and high levels of attraction and low level of infrastructure. In addition to that, the results show that accessibility is not a inhibitory factor for the tourists to reach the destination and the level of degradation is very low in the area. Therefore, a planned research approach is necessary to investigate the areas with high tourism development potential and relatively untouched. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of The 2nd International Geography Symposium-Mediterranean Environment
Session Context Data Integration To Address the Cold Start Problem in E-Commerce Recommender Systems
(Elsevier, 2024) Esmeli, Ramazan; Abdullahi, Hassana; Bader-El-Den, Mohamed; Can, Ali Selcuk
Recommender systems play an important role in identifying and filtering relevant products based on the behaviours of users. Nevertheless, recommender systems suffer from the 'cold-start' problem, which occurs when no prior information about a new session or a user is available. Many approaches to solving the cold-start problem have been presented in the literature. However, there is still room for improving the performance of recommender systems in the cold-start stage. In this article, we present a novel method to alleviate the cold-start problem in session-based recommender systems. The purpose of this work is to develop a session similarity-based cold-start session alleviation approach for recommendation systems. The developed method uses previous sessions' contextual and temporal features to find sessions similar to the newly started one. Our results on three different datasets show that, based on the provided Mean Average Precision and Normalised Discounted Cumulative Gain scores, the Session Similarity-based Framework consistently outperforms baseline models in terms of recommendation relevance and ranking quality across three used datasets. Our approach can be used to address the challenges associated with cold start sessions where no previously interacted items are present.
Understanding Customer Loyalty-Aware Recommender Systems in E-Commerce: an Analytical Perspective
(Springer, 2025) Esmeli, Ramazan; Can, Ali Selcuk; Awad, Aya; Bader-El-Den, Mohamed
The selection of relevant variables is critical for providing personalized product and service recommendations on e-commerce businesses. However, the integration of e-loyalty-related features into recommender systems remains underexplored. This study aims to investigate the impact of incorporating e-loyalty indicators, such as purchase frequency and platform engagement, on the performance of recommender systems in the context of e-commerce businesses. Using three well-established recommender system models and four real-world datasets, we conducted computational experiments to assess performance improvements when e-loyalty features are incorporated. The results show that integrating e-loyalty-related features significantly enhances the performance of recommendation systems, with sequential deep neural networks outperforming other algorithms. Our study contributes to the literature by highlighting the value of leveraging customer loyalty data to enhance recommendation accuracy. Theoretical implications include underscoring the importance of using longitudinal user engagement data in recommender systems to move beyond static personalization toward adaptive, behavior-aware technologies. From a practical perspective, our findings suggest that incorporating e-loyalty features can improve recommendation accuracy, offering valuable insights for e-commerce businesses seeking to personalize their services. This research offers original contributions by focusing on the role of loyalty-driven features in improving recommender systems, an area that remains largely underexplored.