Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks

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How researchers see this paper

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

Relational Collaborative Topic Regression for Recommender Systems (2015, unknown): “Hybrid methods [2], [3], [6], [12], [15], [16], [17], [20], [32], [34], [40], [43], [47], [48] combine both con-”
Sparse linear methods with side information for top-n recommendations (2012, background): “Campos et al [3] combined content-based and collaborative-ﬁltering based recommendations with Bayesian networks, which are composed of item nodes, user nodes and item feature nodes.”
Recommender Systems (2016, unknown): “A unified graph-based representation has been proposed in [129].”
Improving Content-based and Hybrid Music Recommendation using Deep Learning (2014, background): “Hybrid CF and content-based methods 1 have been explored extensively in recommenders for other products such as movies [20, 21, 22, 23].”
Recommender systems survey (2013, methodology): “Hybrid filtering is usually based on bioinspired or probabilistic methods such as genetic algorithms [76,99], fuzzy genetic [7], neural networks [133,62,192], Bayesian networks [50], clustering [209] and latent features [199].”
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, background): “Hybrid approaches [223] exploit the benefits of both collaborative filtering and content-based methods to improve the quality of recommendations.”