Hybrid Web Recommender Systems

By Robin D. Burke. The Adaptive Web, 2007

1,265 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/7af6ffb248de91df369002452980e09751419997

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Citations in the corpus

Data Mining for Web Personalization (2007, background): “A detailed examination of different approaches to create hybrid recommender systems is presented in [21] (see also Chapter 12 of this book [22]).”
User Preferences for Hybrid Explanations (2017, background): “Hybrid recommender systems, which use a combination of signals such as social connections, item attributes, and user behavior, demonstrate improved recommendation accuracy [4].”
Recommender Systems: Introduction and Challenges (2015, methodology): “Consequently, assuming that there is some available knowledge, or possibly none, about the user who is requesting the recommendation, as well as knowledge about items, and other users who received recommendations, the system will leverage this knowledge with an appropriate algorithm to generate vari”
Recommender system application developments: A survey (2015, methodology): “Burke [14] improved Entrée by incorporating CF into KB, which meant that apart from restaurant features, the assessments of users also became criteria.”
Data Mining Methods for Recommender Systems (2015, background): “As a matter of fact, any hybridation technique [16] can be considered an ensemble as it combines in one way or another several classifiers.”
Recommendation to Groups (2007, background): “[34]; Pazzani & Billsus citech10; Smyth [35]; Burke [5]; and Goy & Ardissono [15], many recommender systems do not require their users to specify their preferences explicitly.”
Collaborative filtering recommender systems (2013, methodology): “For arranging a first review of the different kinds of recommender systems, we want to quotation a taxonomy offered by Burke (2007a) that has become a traditional way of identifying between recommender techniques.”
Recommender Systems (2016, neutral): “A discussion of hybrid recommender systems in the specific context of the Web is provided in [118].”
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols (2013, background): “Burke (2007) distinguishes four main recommendation techniques: content-based techniques (CB), which suggest to the target user similar items to those preferred by her in the past, collaborative filtering techniques (CF), which suggest items preferred by users with similar tastes to the target…”
Context-aware recommender systems (2008, positive): “mance improvements over the individual approaches [23, 24, 42, 55].”
Content-Based Recommendation Systems (2007, background): “Chapter 12 of this book [8] discusses a variety of other approaches to combining content and collaborative information in recommendation systems.”
Recommendation systems: Principles, methods and evaluation (2015, methodology): “A hybrid recommender algorithm is employed by many applications as a result of new user problem of content-based filtering techniques and average user problem of collaborative filtering [26].”
Sequence-Aware Recommender Systems (2018, background): “Hybrid approaches are often used to build recommendation systems due to their capability of overcoming the shortcomings of individual methods, e.g., in terms of limited content discovery support or in the context of user-or item cold start situations [16].”
Group recommendations with rank aggregation and collaborative filtering (2010, methodology): “In such case, we would compute nDCG on the ranked list [1, 4, 8, 7, 9].”
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources (2017, background): “is leads to research on hybrid recommendation techniques [6, 7].”
Recommender Systems (2012, methodology): “Hybrid methods overcome this problem by aptly combining recommendations obtained by different methods [312, 313].”
Research-paper recommender systems: a literature survey (2015, background): “In true hybrids, the combined concepts are similarly important [303,337].”
Collaborative Filtering Recommender Systems (2007, background): “For a more detailed look at these techniques, refer to Chapter 12 of this book [ 10 ].”
Introduction to Recommender Systems Handbook (2011, methodology): “an algorithms prediction [25] and related measures, this chapter is unique in its approach to the evaluation discussion since it focuses on property-directed evaluation.”