{"product_id":"recommender-systems-3","title":"Recommender Systems","description":"\u003cp\u003eRecommender systems use information filtering to predict user preferences. They are becoming a vital part of e-business and are used in a wide variety of industries, ranging from entertainment and social networking to information technology, tourism, education, agriculture, healthcare, manufacturing, and retail. \u003cb\u003eRecommender Systems: Algorithms and Applications \u003c\/b\u003edives into the theoretical underpinnings of these systems and looks at how this theory is applied and implemented in actual systems. \u003c\/p\u003e\u003cp\u003eThe book examines several classes of recommendation algorithms, including\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e \u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eMachine learning algorithms\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e \u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eCommunity detection algorithms\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e \u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eFiltering algorithms\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eVarious efficient and robust product recommender systems using machine learning algorithms are helpful in filtering and exploring unseen data by users for better prediction and extrapolation of decisions. These are providing a wider range of solutions to such challenges as imbalanced data set problems, cold-start problems, and long tail problems. This book also looks at fundamental ontological positions that form the foundations of recommender systems and explain why certain recommendations are predicted over others. \u003c\/p\u003e\u003cp\u003eTechniques and approaches for developing recommender systems are also investigated. These can help with implementing algorithms as systems and include\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e \u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eA latent-factor technique for model-based filtering systems\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e \u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eCollaborative filtering approaches\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e \u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eContent-based approaches\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eFinally, this book examines actual systems for social networking, recommending consumer products, and predicting risk in software engineering projects. \u003c\/p\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default Title","offer_id":54244676436312,"sku":"9780367631857","price":127.99,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0278\/1295\/4195\/files\/9780367631857_d0d5ffa4-5277-4fef-b2a2-21d0c6606300.jpg?v=1770906625","url":"https:\/\/agendabookshop.com\/products\/recommender-systems-3","provider":"Agenda Bookshop","version":"1.0","type":"link"}