Recommender Algorithms by Aliev Rauf (16 results)

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Paperback. Condition: new. Paperback. This book serves as an essential practitioner's guide to the world of recommender algorithms as it stands in early 2026. We begin with the indispensable baselines-from classic neighborhood models to powerful matrix factorization-and build toward the sophisticated deep learning architectures that power today's largest platforms, including hybrids for CTR prediction and state-of-the-art sequential models. A core theme of this guide is the practical integration of the latest technological breakthroughs. We dedicate significant attention to the transformative impact of Large Language Models (LLMs), offering architectural blueprints for leveraging them as powerful semantic feature extractors, building reliable Retrieval-Augmented Generation (RAG) pipelines, and designing the next wave of generative and conversational recommender agents. Furthermore, we explore the critical role of multimodal models like CLIP for solving visual cold-start problems and provide insights into specialized areas like debiasing and fairness. This is more than a survey; it is a toolkit for the modern engineer. Each section balances conceptual depth with pragmatic advice on implementation, scalability, and production readiness, making it the definitive resource for professionals tasked with creating value through personalization. Foundational and Heuristic-Driven AlgorithmsVector Space Model (VSM)TF-IDFEmbedding-based Similarity (Word2Vec)CBOW (Continuous Bag-of-Words)FastTextClassic Rule-Based SystemsTop PopularApriori / FP-Growth / EclatInteraction-Driven Recommendation AlgorithmsItemKNN / UserKNNSARSlopeOneAttribute-Aware k-NNFunkSVDPMFWRMFBPRSVD++TimeSVD++SLIM & FISMNon-Negative Matrix Factorization (NonNegMF)CMLNCF & NeuMFDeepFM & xDeepFMAutoencoder-based (DAE & VAE)SimpleXEASEGRU4RecNextItNetSASRec & BERT4RecCL4SRecTBGRecallIRGANDiffRecGFN4RecIDNP (Interest Dynamics Neural Process)WMFBPR (Weighted MF + BPR)ASVD (Asymmetric SVD)SKNN (Session-Based KNN)Text-Driven Recommendation AlgorithmsDeepCoNNNARREMultimodal Recommendation AlgorithmsCLIPALBEF (Align Before Fuse)Context-Aware Recommendation AlgorithmsFactorization Machines (FM)AMF (Attentional Factorization Machine)Wide & DeepGBDTXGBoosLightGBMDCNKnowledge-Aware Recommendation AlgorithmsNGCFLightGCNSGLEmbedding-based (CKE, KTUP)Path-based (RippleNet)GNN-based (KGCN, KGAT, KGIN)Specialized Recommendation TasksMF-IPSCausEFairRecCMFCoNetMeLUNew Algorithmic ParadigmsReinforcement Learning (RL) for RecSysCausal Inference in RecSysInverse Propensity Scoring (IPS)Doubly Robust (DR) MethodsUplift ModelingSCM-Based Debiasing (PDA, DecRS, IV4Rec)Counterfactuals (CauseRec, PSF-RS, CountER)Explainable AI (XAI) for RecSysFairness-Aware RecSysDiversity and Novelty Optimization (MMR)Please be aware that the depth of explanation varies across different algorithms. Foundational concepts may be covered in greater detail, while others are presented more concisely. Complimentary app: Complimentary app (deployed): https: //recommender-algorithms.streamlit.app/ This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. This book serves as an essential practitioner's guide to the world of recommender algorithms as it stands in early 2026. We begin with the indispensable baselines-from classic neighborhood models to powerful matrix factorization-and build toward the sophisticated deep learning architectures that power today's largest platforms, including hybrids for CTR prediction and state-of-the-art sequential models. A core theme of this guide is the practical integration of the latest technological breakthroughs. We dedicate significant attention to the transformative impact of Large Language Models (LLMs), offering architectural blueprints for leveraging them as powerful semantic feature extractors, building reliable Retrieval-Augmented Generation (RAG) pipelines, and designing the next wave of generative and conversational recommender agents. Furthermore, we explore the critical role of multimodal models like CLIP for solving visual cold-start problems and provide insights into specialized areas like debiasing and fairness. This is more than a survey; it is a toolkit for the modern engineer. Each section balances conceptual depth with pragmatic advice on implementation, scalability, and production readiness, making it the definitive resource for professionals tasked with creating value through personalization. Foundational and Heuristic-Driven AlgorithmsVector Space Model (VSM)TF-IDFEmbedding-based Similarity (Word2Vec)CBOW (Continuous Bag-of-Words)FastTextClassic Rule-Based SystemsTop PopularApriori / FP-Growth / EclatInteraction-Driven Recommendation AlgorithmsItemKNN / UserKNNSARSlopeOneAttribute-Aware k-NNFunkSVDPMFWRMFBPRSVD++TimeSVD++SLIM & FISMNon-Negative Matrix Factorization (NonNegMF)CMLNCF & NeuMFDeepFM & xDeepFMAutoencoder-based (DAE & VAE)SimpleXEASEGRU4RecNextItNetSASRec & BERT4RecCL4SRecTBGRecallIRGANDiffRecGFN4RecIDNP (Interest Dynamics Neural Process)WMFBPR (Weighted MF + BPR)ASVD (Asymmetric SVD)SKNN (Session-Based KNN)Text-Driven Recommendation AlgorithmsDeepCoNNNARREMultimodal Recommendation AlgorithmsCLIPALBEF (Align Before Fuse)Context-Aware Recommendation AlgorithmsFactorization Machines (FM)AMF (Attentional Factorization Machine)Wide & DeepGBDTXGBoosLightGBMDCNKnowledge-Aware Recommendation AlgorithmsNGCFLightGCNSGLEmbedding-based (CKE, KTUP)Path-based (RippleNet)GNN-based (KGCN, KGAT, KGIN)Specialized Recommendation TasksMF-IPSCausEFairRecCMFCoNetMeLUNew Algorithmic ParadigmsReinforcement Learning (RL) for RecSysCausal Inference in RecSysInverse Propensity Scoring (IPS)Doubly Robust (DR) MethodsUplift ModelingSCM-Based Debiasing (PDA, DecRS, IV4Rec)Counterfactuals (CauseRec, PSF-RS, CountER)Explainable AI (XAI) for RecSysFairness-Aware RecSysDiversity and Novelty Optimization (MMR)Please be aware that the depth of explanation varies across different algorithms. Foundational concepts may be covered in greater detail, while others are presented more concisely. Complimentary app: Complimentary app (deployed): https: //recommender-algorithms.streamlit.app/ This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. This book serves as an essential practitioner's guide to the world of recommender algorithms as it stands in early 2026. We begin with the indispensable baselines-from classic neighborhood models to powerful matrix factorization-and build toward the sophisticated deep learning architectures that power today's largest platforms, including hybrids for CTR prediction and state-of-the-art sequential models. A core theme of this guide is the practical integration of the latest technological breakthroughs. We dedicate significant attention to the transformative impact of Large Language Models (LLMs), offering architectural blueprints for leveraging them as powerful semantic feature extractors, building reliable Retrieval-Augmented Generation (RAG) pipelines, and designing the next wave of generative and conversational recommender agents. Furthermore, we explore the critical role of multimodal models like CLIP for solving visual cold-start problems and provide insights into specialized areas like debiasing and fairness. This is more than a survey; it is a toolkit for the modern engineer. Each section balances conceptual depth with pragmatic advice on implementation, scalability, and production readiness, making it the definitive resource for professionals tasked with creating value through personalization. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condition: Neu. Recommender Algorithms | Rauf Aliev | Taschenbuch | Englisch | 2025 | Rauf Aliev | EAN 9798232638405 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.