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A Personalized Context-Aware Places of Interest Recommender System
Journal of Computing Theories and Applications
Vol 2
, No 4
(2025)
This research presents a personalized, context-aware recommender system to suggest Places of Interest (POIs) using a hybrid approach combining Bayesian inference and collaborative filtering. The system explicitly addresses the cold-start problem that new users face and improves recommendation accuracy by considering contextual variables such as user mood, budget, companion, and location. The system collects real-time contextual inputs for new users with no historical data and applies Bayesian in...
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Comprehensive Evaluation of LDA, NMF, and BERTopic's Performance on News Headline Topic Modeling
Journal of Computing Theories and Applications
Vol 2
, No 2
(2024)
Topic modeling is an integral text mining component, employing diverse algorithms to uncover hidden themes within texts. This study examines the comparative performance of prominent topic modeling techniques on news headlines, which is characterized by brevity and specific linguistic style. Given the corpus originates from a non-native English-speaking country, an additional layer of complexity is introduced to the task. Our research explores the feasibility of employing a committee approach for...
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