Recommender Systems and Collaborative Filtering

Last update: 12 Dec 2024 09:58
First version: 14 August 2019

Yet Another Inadequate Placeholder

An ubiquitous application of data mining.

One of the things I find technically interesting here is the large role for factor models. This makes all kinds of sense, starting with the potential simplicity of the technique --- you can (I have!) teach it to undergrads whose prior training ends at linear regression. (Needless to say industrial-strength implementations are a bit more involved, but the ideas are the same.) But, unlike many other applications of factor models, here people seem un-interested in "reifying" the factors, and indeed uninterested in interpreting them.

--- Some day, an essay will be written about how we got from the collectivist optimism of "collaborative filtering" (as in Shardanand and Maes 1995) to being followed around the Internet by intrusive and inappropriate ads. This is not that day (and I may not be the right person).

Andrey Feuerverger, Yu He, and Shashi Khatri, "Statistical Significance of the Netflix Challenge", Statistical Science 27 (2012): 202--231
Paul Resnick, "Filtering Information on the Internet", Scientific American March, 1997
Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl, "GroupLens: an open architecture for collaborative filtering of netnews", pp. 175--186 in Proceedings of the 1994 ACM conference on Computer supported cooperative work [CSCW '94] [HTML reprint via Prof. Resnick]
Paul Resnick and Hal R. Varian, "Recommender Systems", Communications of the ACM 40 (1997): 56--58 [Introduction to special section of the issue, pp. 59--89]
Upendra Shardanand and Pattie Maes, "Social Information Filtering: Algorithms for Automating 'Word of Mouth'", pp. 210--217 in CHI '95: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems [PDF version of the HTML of the conference proceedings version; PDF preprint via CiteSeer]

Gérard Biau, Benoît Cadre, Laurent Rouvière
- "Statistical analysis of $k$-nearest neighbor collaborative recommendation", Annals of Statistics 38 (2010): 1568--1592, arxiv:1010.0499
- "A Stochastic Model for Collaborative Recommendation", arxiv:0910.2340
Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach, "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches", Proceedings of the 13th ACM Conference on Recommender Systems (RecSys 2019), arxiv:1907.06902
Antonia Godoy-Lorite, Roger Guimerà, Cristopher Moore, and Marta Sales-Pardo, "Accurate and scalable social recommendation using mixed-membership stochastic block models", Proceedings of the National Academy of Sciences (USA) 113 (2016): 14207--14212
Benjamin Marlin, Richard S. Zemel, Sam Roweis, Malcolm Slaney, "Collaborative Filtering and the Missing at Random Assumption", UAI 2007, arxiv:1206.5267
Amit Sharma, Jake M. Hofman and Duncan J. Watts, "Estimating the Causal Impact of Recommendation Systems from Observational Data", pp. 453--470 in Michal Feldman, Michael Schwarz and Tim Roughgarden (eds.), Proceedings of the Sixteenth ACM Conference on Economics and Computation [EC '15], arxiv:1510.05569
Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei, "The Deconfounded Recommender: A Causal Inference Approach to Recommendation", arxiv:1808.06581

The lecture notes on recommender systems in my data mining class

Jacob Abernethy, Francis Bach, Theodoros Evgeniou, Jean-Philippe Vert , "A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization", arxiv:0802.1430
Dhoha Almazro, Ghadeer Shahatah, Lamia Albdulkarim, Mona Kherees, Romy Martinez, William Nzoukou, "A Survey Paper on Recommender Systems", arxiv:1006.5278
Newsha Ardalani, Carole-Jean Wu, Zeliang Chen, Bhargav Bhushanam, Adnan Aziz, "Understanding Scaling Laws for Recommendation Models", arxiv:2208.08489
Marcel Blattner, Matus Medo, "Recommendation systems in the scope of opinion formation: a model", arxiv:1206.3924
Keith Burghardt, Kristina Lerman, "Emergent Instabilities in Algorithmic Feedback Loops", arxiv:2201.07203
Sarah Dean, Jamie Morgenstern, "Preference Dynamics Under Personalized Recommendations", arxiv:2205.13026
Aaron Defazio, Tiberio Caetano, "A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training", arxiv:1206.4622
Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean, "Modeling Content Creator Incentives on Algorithm-Curated Platforms", arxiv:2206.13102
Rinat Khaziev, Bryce Casavant, Pearce Washabaugh, Amy A. Winecoff, Matthew Graham, "Recommendation or Discrimination?: Quantifying Distribution Parity in Information Retrieval Systems", arxiv:1909.06429
Yihua Li, Devavrat Shah, Dogyoon Song, Christina Lee Yu, "Nearest Neighbors for Matrix Estimation Interpreted as Blind Regression for Latent Variable Model", arxiv:1705.04867
Kun Lin, Nasim Sonboli, Bamshad Mobasher, Robin Burke, "Crank up the volume: preference bias amplification in collaborative recommendation", arxiv:1909.06362 [Surely there's a question here about the extent to which stated preferences are real preferences.]
Arvind Narayanan, "Understanding Social Media Recommendation Algorithms", Knight First Amendment Institute 9 March 2023