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Full-Text Articles in Computer Engineering
Cosine-Based Explainable Matrix Factorization For Collaborative Filtering Recommendation., Pegah Sagheb Haghighi
Cosine-Based Explainable Matrix Factorization For Collaborative Filtering Recommendation., Pegah Sagheb Haghighi
Electronic Theses and Dissertations
Recent years saw an explosive growth in the amount of digital information and the number of users who interact with this information through various platforms, ranging from web services to mobile applications and smart devices. This increase in information and users has naturally led to information overload which inherently limits the capacity of users to discover and find their needs among the staggering array of options available at any given time, the majority of which they may never become aware of. Online services have handled this information overload by using algorithmic filtering tools that can suggest relevant and personalized information …
Multi-Style Explainable Matrix Factorization Techniques For Recommender Systems., Olurotimi Nugbepo Seton
Multi-Style Explainable Matrix Factorization Techniques For Recommender Systems., Olurotimi Nugbepo Seton
Electronic Theses and Dissertations
Black-box recommender system models are machine learning models that generate personalized recommendations without explaining how the recommendations were generated to the user or giving them a way to correct wrong assumptions made about them by the model. However, compared to white-box models, which are transparent and scrutable, black-box models are generally more accurate. Recent research has shown that accuracy alone is not sufficient for user satisfaction. One such black-box model is Matrix Factorization, a State of the Art recommendation technique that is widely used due to its ability to deal with sparse data sets and to produce accurate recommendations. Recent …