图书简介
Recommender systems are very popular nowadays, as both an academic research field and services provided by numerous companies for e-commerce, multimedia and Web content. Collaborative-based methods have been the focus of recommender systems research for more than two decades.
The unique feature of the compendium is the technical details of collaborative recommenders. The book chapters include algorithm implementations, elaborate on practical issues faced when deploying these algorithms in large-scale systems, describe various optimizations and decisions made, and list parameters of the algorithms.
This must-have title is a useful reference materials for researchers, IT professionals and those keen to incorporate recommendation technologies into their systems and services.
Preface; Algorithms: Collaborative Filtering Based on User-Item Interaction Matrices and Session Logs; Matrix Factorization for Collaborative Recommendations; Cutting-Edge Collaborative Recommendation Algorithms: Deep Learning ; Hybrid Collaborative Recommendations: Practical Considerations and Tools to Develop a Recommender; Context-Aware Recommendations; Group Recommendations; Practical Challenges: User Preference Sources: Explicit vs. Implicit Feedback; User Preference Elicitation, Rating Sparsity and Cold Start; Offline and Online Evaluation of Recommendations; Recommendations Biases and Beyond-Accuracy Objectives in Collaborative Filtering; Scalability and Distribution of Collaborative Recommenders; Robustness and Attacks on Recommenders; Privacy in Collaborative Recommenders; Applications: Music Recommendations; Contact Recommendations in Social Networks; Job Recommendations: The Xing Case; Academic Recommendations: The Mendeley Case; MoocRec.com: Massive Open Online Courses Recommender System; Food Recommendations; Clothing Recommendations: The Zalando Case; TV and Movie Recommendations: The Comcast Case
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