Introduction to Recommender Systems Handbook

By Francesco Ricci, Lior Rokach, Bracha Shapira. 2011

2,737 citations recorded by the source corpus.

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

How researchers see this paper

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

In Artificial Intelligence

1. Yongfeng Zhang
2. Xu Chen
3. Xiangnan He
4. Wayne Xin Zhao
5. Hongzhi Yin
6. Dit-Yan Yeung
7. Xing Xie
8. Hao Wang

In Computer Vision and Pattern Recognition

1. Yongfeng Zhang
2. Wayne Xin Zhao
3. Xu Chen
4. Hao Wang
5. Xiangnan He
6. Balázs Hidasi
7. Domonkos Tikk
8. Yong Li

In Information Systems

1. Yongfeng Zhang
2. Xu Chen
3. Xiangnan He
4. Xing Xie
5. Hongzhi Yin
6. Wayne Xin Zhao
7. Min Zhang
8. Yiqun Liu

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “Seung [1999, 2001]), Max-Margin Matrix Factorization (MMMF) (Srebro et al. [2005], Rennie and Srebro [2005]), Probabilistic Matrix Factorization (PMF) (Mnih and Salakhutdinov [2008], Salakhutdinov and Mnih [2008]), and Localized Matrix Factorization (LMF) (Zhang et al.”
• Marketing robot services in hospitality and tourism: the role of anthropomorphism (2019, positive): “Conversational styles similar to those explored for recommender systems (Ricci, Rokach, & Shapira, 2011) could also help design robots for better rService quality.”
• Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning (2022, background): “In the age of information explosion, recommender systems occupy an important position to discover users’ preferences and deliver online services efficiently [23].”
• Neural Attentional Rating Regression with Review-level Explanations (2018, background): “In recent years, matrix factorization (MF) has become the most popular collaborative ltering approach[35, 38].”
• Reinforcement Learning based Recommender Systems: A Survey (2021, methodology): “In a recommendation problem, the RS algorithm, through interaction with the user and receiving their implicit/explicit feedback, tries to recommend the best items to the user, in order to achieve the goal it is designed for, which could be increasing profit, user satisfaction, or user fidelity [3].”
• Mobile recommender systems in tourism (2014, methodology): “RSs use details of registered user profiles and habits of the whole user community to compare available information items against reference characteristics in order to present item recommendations [4],[70].”
• Deep reinforcement learning for page-wise recommendations (2018, background): “popular in recent years, and have been utilized in a variety of domains including movies, music, books, search queries, and social tags[25, 26].”
• Multi-behavior Recommendation with Graph Convolutional Networks (2020, background): “Personalized recommender system has become a widely used service to alleviate the issue of information overload nowadays [28].”
• Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback (2012, background): “For instance, a recommender system may consider the navigation to a particular product page as an implicit sign of preference for the item shown on that page [9].”
• Personalized Machine Translation: Predicting Translational Preferences (2015, methodology): “Cosine similarity is a popular function for that purpose, and we also use it in our work (Resnick et al., 1994; Sarwar et al., 2001; Ricci et al., 2011).”
• Self-Supervised Learning for Recommender Systems: A Survey (2022, background): “R ECOMMENDERsystemshavebecomeavitaltoolfordis-coveringusers’latentinterestsandpreferences,providing delightful user experience, and driving incremental revenue in various online E-commerce platforms [1].”
• Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks (2016, methodology): “Existing methods for recommender systems can be roughly categorized into three classes [13]: content-based methods that use the user profiles or product descriptions only, collaborative filtering (CF) based methods that use the ratings only, and hybrid methods that make use of both.”
• Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond (2021, background): “The recommendation system [139] is a subclass of the information retrieval domain”