## Recommender Systems and Collaborative Filtering

*07 May 2022 16:31*

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.

See also: Collective Cognition; Factor Models

- Recommended (you should pardon the expression), origins and big picture:
- 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]

- Recommended, technical close-ups:
- 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

- "Statistical analysis of $k$-nearest neighbor collaborative recommendation",
Annals of Statistics
- 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

- Modesty forbids me to recommend:
- The lecture notes on recommender systems in my data mining class

- To read:
- 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
- Marcel Blattner, Matus Medo, "Recommendation systems in the scope of opinion formation: a model", arxiv:1206.3924
- Aaron Defazio, Tiberio Caetano, "A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training", arxiv:1206.4622
- Rinat Khaziev, Bryce Casavant, Pearce Washabaugh, Amy A. Winecoff, Matthew Graham, "Recommendation or Discrimination?: Quantifying Distribution Parity in Information Retrieval Systems", arxiv:1909.06429
- 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.]