Recommender Systems: Introduction and Challenges

By Francesco Ricci, Lior Rokach, Bracha Shapira. Recommender Systems Handbook, 2015

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Tailored gamification: A review of literature (2020, background): “Recommendation technique also reuses this user model, given that “recommendations are usually personalized, different users [...] benefit from diverse, tailored suggestions” (Ricci et al., 2015).”
Deep Learning Based Recommender System (2017, background): “ introducing deep neural networks to recommender systems. 2.1 Recommender Systems Recommender systems estimate users’ preference on items and recommend items that users might like to them proactively [1, 121]. Recommendation models are usually classi•ed into three categories [1, 69]: collaborative •”
Generate Natural Language Explanations for Recommendation (2021, background): “Collaborative Filtering (CF) has been one of the most successful approaches to generate recommendations based on historical user behaviors [29].”
A survey of active learning in collaborative filtering recommender systems (2016, methodology): “And to recommend restaurants it is possible to use information about the restaurant cuisine or location [32,23,17].”
Practical Privacy Preserving POI Recommendation (2020, background): “Therefore, both data and models of most existing recommender systems could be in high privacy risks [43, 48].”