Hybrid Recommender Systems: Survey and Experiments

By Robin D. Burke. 2002

4,482 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Information Systems

Citations in the corpus

Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs (2017, background): “By combining multiple techinques, hybrid recommender systems can be developed to handle a variety of complex scenarios [8,17].”
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources (2017, background): “is leads to research on hybrid recommendation techniques [6, 7].”
Recommender system application developments: A survey (2015, methodology): “According to Burke [27], there are seven basic hybridization mechanisms of combinations used in recommender systems to build hybrids: weighted [28], mixed [29], switching [30], feature combination, feature augmentation [31] [32], cascade [14] and meta-level [33].”
A probabilistic model to resolve diversity–accuracy challenge of recommendation systems (2015, background): “In many applications, content-based features are not easy to extract, and thus collaborative filtering approaches are the preferred ones.”
TasteWeights: a visual interactive hybrid recommender system (2012, background): “Burke Figure 2: Additional info shown when an item gets clicked: (a) profile, recommendation, or Wikipedia item (b) Facebook friend (c) Twitter expert [4] suggests that recommender systems have three distinct parts: input, background, and suggestions.”
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols (2013, background): “There are many variants of both approaches in the literature, and also hybrid combinations aimed to solve weaknesses from each one—(Adomavicius and Tuzhilin 2005) and (Burke 2002) provide extensive surveys on these topics.”
Explaining Recommendations: Design and Evaluation (2015, methodology): “In the following sections we will give further examples of how explanation styles can be inspired by common algorithms as classified by Burke [12].”
Offline and online evaluation of news recommender systems at swissinfo.ch (2014, background): “There exist many news recommender systems [1, 2, 3, 10, 14], however we decided to select only these baselines for”
Feedback effects between similarity and social influence in online communities (2008, background): “This suggests future work investigating combining the two in a hybrid recommender system [ 3 ] may be a promising approach, especially in domains such as Facebook where social information and easily-computable similarity information coexist.”
Mobile recommender systems in tourism (2014, methodology): “Hybrid RSs (Burke, 2002): Those systems use a combination of the abovementioned methods, exploiting the advantages of one technique to compensate the shortcomings of another, thereby improving the overall performance.”
Recommender systems survey (2013, methodology): “Bogers and Van Den Bosh [39] examine how to incorporate the tags and other metadata into a hybrid CBF/CF algorithm by replacing the traditional user-based and item-based similarity measures by tag overlap.”
Stacking recommendation engines with additional meta-features (2009, methodology): “In our system, component engines are level-1 predictors, and a level-2 predictor is learned to generate the final prediction of the hybrid system.”
Relational Collaborative Topic Regression for Recommender Systems (2015, methodology): “Hybrid methods [2], [3], [6], [12], [15], [16], [17], [20], [32], [34], [40], [43], [47], [48] combine both content-based methods and CF based methods by ensemble techniques.”
Contextual Combinatorial Bandit and its Application on Diversified Online Recommendation (2014, background): “Most traditional recommendation techniques focus on learning user preference according to users’ historical records [12, 23, 6].”
Explaining Recommendations: Satisfaction vs. Promotion (2005, background): “A survey of hybrid recommenders can be found at [3].”
A survey of multiple classifier systems as hybrid systems (2014, background): “Burke [222] proposed hybrid recommender systems combining two or more”
Constraint-based recommender systems: technologies and research issues (2008, background): “[7] [14] [33] are an attempt to provide automated assistance for such decision tasks.”