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Full-Text Articles in Engineering
Mining Semantic Knowledge Graphs To Add Explainability To Black Box Recommender Systems, Mohammed Alshammari, Olfa Nasraoui, Scott Sanders
Mining Semantic Knowledge Graphs To Add Explainability To Black Box Recommender Systems, Mohammed Alshammari, Olfa Nasraoui, Scott Sanders
Faculty Scholarship
Recommender systems are being increasingly used to predict the preferences of users on online platforms and recommend relevant options that help them cope with information overload. In particular, modern model-based collaborative filtering algorithms, such as latent factor models, are considered state-of-the-art in recommendation systems. Unfortunately, these black box systems lack transparency, as they provide little information about the reasoning behind their predictions. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less accurate than sophisticated black box models. Recent research has demonstrated that explanations are an essential component in bringing the powerful predictions of …
Indoor Scene Generation Based On Case-Based Reasoning And Collaborative Filtering, Peihua Song, Jinyuan Jia
Indoor Scene Generation Based On Case-Based Reasoning And Collaborative Filtering, Peihua Song, Jinyuan Jia
Journal of System Simulation
Abstract: To solve the problem of time-consuming and single scene generation in indoor scene generation, we propose an indoor scene generation algorithm based on case-based reasoning and collaborative filtering techniques. The algorithm of functional area division is performed on the two-dimensional room floor plan. Thecase-based reasoning is used to generate 3D scenes; and the collaborative filtering is used to generate diverse indoor scenes. A scene evaluation method based on user feedback information is proposed. Experiments were carried out on the living room and bedroom to generate scenes. The experimental results show that the proposed algorithm is effective. The running time …
Evolutionary Approaches For Weight Optimization In Collaborative Filtering-Based Recommender Systems, Sevgi̇ Yi̇ği̇t Sert, Yilmaz Ar, Gazi̇ Erkan Bostanci
Evolutionary Approaches For Weight Optimization In Collaborative Filtering-Based Recommender Systems, Sevgi̇ Yi̇ği̇t Sert, Yilmaz Ar, Gazi̇ Erkan Bostanci
Turkish Journal of Electrical Engineering and Computer Sciences
Collaborative filtering is one of the widely adopted approaches in recommender systems used for e-commerce applications, stating that users having similar tastes will have similar preferences in the future. The literature presents a number of similarity metrics such as the extended Jaccard coefficient to quantify these preference similarities. This paper aims to improve prediction accuracy by optimizing the similarity values computed using these metrics by adopting two biologically inspired approaches, namely artificial bee colony and genetic algorithms, with a bottom-up approach, suggesting that any improvement on a single-user basis will reflect on the overall prediction accuracy. Detailed statistical analysis was …