Reality-Mining with Smartphones: Detecting and Predicting Life Events based on App Installation Behavior

Life events are often described as major forces that are going to shape tomorrow\u27s consumer need, behavior and mood. Thus, the prediction of life events is highly relevant in marketing and sociology. In this paper, we propose a data-driven, real-time method to predict individual life events, using readily available data from smartphones. Our large-scale user study with more than 2000 users shows that our method is able to predict life events with 64.5% higher accuracy, 183.1% better precision and 88.0% higher specificity than a random model on average

Privacy-preserving collaborative recommendations based on random perturbations

© 2016 Elsevier Ltd Collaborative recommender systems offer a solution to the information overload problem found in online environments such as e-commerce. The use of collaborative filtering, the most widely used recommendation method, gives rise to potential privacy issues. In addition, the user ratings utilized in collaborative filtering systems to recommend products or services must be protected. The purpose of this research is to provide a solution to the privacy concerns of collaborative filtering users, while maintaining high accuracy of recommendations. This paper proposes a multi-level privacy-preserving method for collaborative filtering systems by perturbing each rating before it is submitted to the server. The perturbation method is based on multiple levels and different ranges of random values for each level. Before the submission of each rating, the privacy level and the perturbation range are selected randomly from a fixed range of privacy levels. The proposed privacy method has been experimentally evaluated with the results showing that with a small decrease of utility, user privacy can be protected, while the proposed approach offers practical and effective results

The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias

Collaborative filtering-based recommender systems leverage vast amounts of behavioral user data, which poses severe privacy risks. Thus, often, random noise is added to the data to ensure Differential Privacy (DP). However, to date, it is not well understood, in which ways this impacts personalized recommendations. In this work, we study how DP impacts recommendation accuracy and popularity bias, when applied to the training data of state-of-the-art recommendation models. Our findings are three-fold: First, we find that nearly all users' recommendations change when DP is applied. Second, recommendation accuracy drops substantially while recommended item popularity experiences a sharp increase, suggesting that popularity bias worsens. Third, we find that DP exacerbates popularity bias more severely for users who prefer unpopular items than for users that prefer popular items.Comment: Accepted at the IR4Good track at ECIR'24, 17 page

Job Seekers\u27 Acceptance of Job Recommender Systems: Results of an Empirical Study

Based on UTAUT2 and the importance of trust to explain user behavior in relation to recommender systems, we focus on job recommender systems by developing and validating a job recommender system acceptance model. The results of our empirical, survey-based study with 440 job seekers indicate that beside performance expectancy and habit, trust is among the three most important determinants and it is especially relevant for women, passive job seekers and those without experience in using job recommender systems. The paper extends general trust and recommender system research by revealing three moderators for the trust and intention relationship. It contextualizes the UTAUT2 by incorporating trust as an antecedent of a consumer’s intention to use and by revealing three moderating effects for this relationship. Hence, it is the basis for further studies investigating the acceptance of job recommender system, which has rather been neglected by prior research

Öneri sistemleri ve E- ticarette öneri sistemlerinin kullanımı

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Anahtar kelimeler: Öneri sistemleri, içerik bazlı öneri sistemleri, işbirlikçi öneri sistemleri, hibrit içerik sistemleri, e-ticaret. İnternet üzerinden alışveriş, artık hayatımızda alışveriş alışkanlıklarımızı büyük bir ölçüde değiştirdi ve değiştirmeye de devam ediyor. Günün her saatinde her kategoride ürüne ulaşmamız için ihtiyacımız olan sadece internetle birlikte bir bilgisayar, akıllı telefon veya bir tablet. Bu sayede, ihtiyaç duyduğumuz veya almayı düşündüğümüz herhangi bir ürün için farklı alternatiflere, farklı kalite ve fiyatlara ulaşmamız artık çok kolay. Yaşadığımız ülkenin hatta dünyanın herhangi bir noktasından kapımıza kadar ürünün teslimini sağlayabiliyoruz. Öneri sistemleri, alınması düşünülen ürünler için aynı veya benzer ürünleri önceden almış başka müşterilerin yorum, değerlendirme ve oylarının da yardımıyla alternatif seçenekler veya tamamlayıcı başka ürünler önererek yapılacak alışverişi çok kolaylaştırmaktadır. Bu sayede zamandan tasarruf edilmesi, ihtiyaca daha yakın ürünlerin incelenmesi sağlanmış olur.Keywords: Recommendation systems, content-based recommendation systems, collaborative recommendation systems, hybrid recommendation systems, e-commerce Online shopping has changed our shopping habits to a great extent now and continues to change. In order to reach a product in any categories at any time of the day, we just need to have a computer, a smartphone or a tablet which has Internet connection. Thus, it becomes easy to reach different alternatives, different quality and prices for any product that we need or want to buy. Thanks to Internet, we can deliver the product from any place of the world. Suggestion systems make shopping much easier for the products to be bought by offering alternative options or complementary products with the help of the comments, evaluations and votes of other customers who have already bought the same or similar products for the intended products. Therefore, they help save time and examine the products that are most needed

Introdução aos Sistemas de Recomendação para Grupos

Sistemas de Recomendação tradicionalmente recomendam itens para usuários individuais. Em alguns cenários, entretanto, a recomendação para um grupo de indivíduos é mais adequada. Não existem ainda um bom volume de trabalhos científicos voltados para os chamados Sistemas de Recomendação para Grupos. Uma das grandes peculiaridades desses sistemas é, por exemplo, como lidar adequadamente com as preferências de seus integrantes para geração da recomendação. Com o intuito de contribuir para evolução das pesquisas relacionadas aqui no Brasil, este tutorial trata esta e outras questões relacionadas ao tema. Após apresentar e discutir vários aspectos importantes, tais como, classificações, principais problemáticas, estratégias de agregação de preferências individuais, formas de avaliação, abordagens alternativas em curso e recomendação de sequência de itens, este tutorial contribui ainda ao propor uma nova estratégia de agregação e proceder a todo processo de experimentação adequado, incluindo avaliação comparativa com as estratégias mais estabelecidas da literatura

There's Science in my Fiction! And Other Troubles: How A Recommender System Can Help the Academic Library

In a world with increasing access to raw data, recommender systems can pare down information to help people make choices on a variety of subjects with greater ease. Libraries contain vast amounts of information and use classification schemes to sort it. However, fiction classification is a continuing issue in libraries. This is especially true in academic libraries where fiction might be used for recreational or scholarly purposes. In this paper, the idea that an academic library recommender system might solve some of the problems of fiction classification is discussed. A qualitative evaluation is performed on six book recommender systems. Recommendations given by each system for a single novel are analyzed based upon information gathered from a close reading, book reviews, formal critiques, academic papers, and university syllabi. It is hoped that this study will be of use to academic librarians and creators of recommender systems