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Recommender Systems Handbook
Cross-Domain Recommender Systems
other
Author(s):
Iván Cantador
,
Ignacio Fernández-Tobías
,
Shlomo Berkovsky
,
Paolo Cremonesi
Publication date
(Print):
2015
Publisher:
Springer US
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Socioecological systems
Author and book information
Book Chapter
Publication date (Print):
2015
Pages
: 919-959
DOI:
10.1007/978-1-4899-7637-6_27
SO-VID:
85203e23-ddde-4bb2-99ea-80891991c4c3
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Book chapters
pp. 1
Recommender Systems: Introduction and Challenges
pp. 37
A Comprehensive Survey of Neighborhood-Based Recommendation Methods
pp. 77
Advances in Collaborative Filtering
pp. 119
Semantics-Aware Content-Based Recommender Systems
pp. 161
Constraint-Based Recommender Systems
pp. 191
Context-Aware Recommender Systems
pp. 227
Data Mining Methods for Recommender Systems
pp. 265
Evaluating Recommender Systems
pp. 309
Evaluating Recommender Systems with User Experiments
pp. 353
Explaining Recommendations: Design and Evaluation
pp. 385
Recommender Systems in Industry: A Netflix Case Study
pp. 421
Panorama of Recommender Systems to Support Learning
pp. 453
Music Recommender Systems
pp. 493
The Anatomy of Mobile Location-Based Recommender Systems
pp. 511
Social Recommender Systems
pp. 545
People-to-People Reciprocal Recommenders
pp. 569
Collaboration, Reputation and Recommender Systems in Social Web Search
pp. 611
Human Decision Making and Recommender Systems
pp. 649
Privacy Aspects of Recommender Systems
pp. 689
Source Factors in Recommender System Credibility Evaluation
pp. 715
Personality and Recommender Systems
pp. 743
Group Recommender Systems: Aggregation, Satisfaction and Group Attributes
pp. 777
Aggregation Functions for Recommender Systems
pp. 809
Active Learning in Recommender Systems
pp. 847
Multi-Criteria Recommender Systems
pp. 881
Novelty and Diversity in Recommender Systems
pp. 919
Cross-Domain Recommender Systems
pp. 961
Robust Collaborative Recommendation
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