Information Filtering Algorithm
- It can range from being a Data-Driven Information Filtering Algorithm (such as a collaborative filtering algorithm) to being a Heuristic Information Filtering Algorithm.
- It can range from being a Global Item Recommendation Algorithm to being a Community Item Recommendation Algorithm (for a related users) to being a Personalized Item Recommendation Algorithm.
- It can range from being a Generic Item Recommendation Algorithm Pattern to being a Domain-Specific Item Recommendation Algorithm(e.g for products, for songs, for opponents, ...).
- It can allowing the incorporation of additional information such as implicit feedback, temporal effects, and/or confidence levels.
- See: Information Filtering Algorithm, Classification Algorithm.
- (Wang et al., 2015) ⇒ Hao Wang, Naiyan Wang, and Dit-Yan Yeung. (2015). “Collaborative Deep Learning for Recommender Systems.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783273
- QUOTE: … Existing methods for RS can roughly be categorized into three classes : content-based methods, collaborative filtering (CF) based methods, and hybrid methods. Content-based methods  make use of user profiles or product descriptions for recommendation. CF-based methods [23, 27] use the past activities or preferences, such as user ratings on items, without using user or product content information. Hybrid methods [1, 18, 12] seek to get the best of both worlds by combining content-based and CF-based methods. ...
- (Barbieri et al., 2014) ⇒ Nicola Barbieri, Giuseppe Manco, and Ettore Ritacco. (2014). “Probabilistic Approaches to Recommendations.” In: Synthesis Lectures on Data Mining and Knowledge Discovery, 5(2).
- (Bobadilla et al, 2013) ⇒ Jesús Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez. (2013). “Recommender Systems Survey.” In: Knowledge-based systems 46 (2013): 109-132.
- (Smola, 2012a) ⇒ Alex Smola. (2012). “Recommender Systems.” In: SML: Scalable Machine Learning - STATISTICS 241B, COMPUTER SCIENCE C281B
- Neighborhood methods
- User / movie similarity
- Iteration on graph
- Matrix Factorization
- Ranking and Session Modeling
- Neighborhood methods
- (Koren et al., 2009) ⇒ Yehuda Koren, Robert Bell, and Chris Volinsky. (2009). “Matrix Factorization Techniques for Recommender Systems.” Computer 42, no. 8
- (Wei et al., 2005) ⇒ Yan Zheng Wei, Luc Moreau, and Nicholas R. Jennings. (2005). “A Market-based Approach to Recommender Systems.” In: ACM Transactions on Information Systems (TOIS), 23(3) doi:10.1145/1080343.1080344.