Computing Reviews
Today's Issue Hot Topics Search Browse Recommended My Account Log In
Review Help
Search
An optimization model for collaborative recommendation using a covariance-based regularizer
Lecron F., Fouss F. Data Mining and Knowledge Discovery32 (3):651-674,2018.Type:Article
Date Reviewed: Apr 10 2019

In the era of big data, we are surrounded by recommendation systems that leverage and predict our responses, from daily shopping patterns to political campaigns and even presidential elections. There are various techniques and algorithms available for collaborative recommendation (in a broad sense, collaborative filtering), including memory based, regression based, objective function based, and so on. These systems are able to automatically suggest closely related options from vastly available information, beyond any human ability to manually deal with it. Nowadays, state-of-the-art recommendation systems provide fairly accurate recommendations in many areas. However, as the authors mention, optimizations are needed for recommender systems with, for example, multiple constraints. This paper proposes an optimization model with a covariance-based regularizer.

For a recommendation system with users u and items v, the proposed model starts with a sample matrix Mv×u, where mij represents the preference of user j on item i. The Mahalanobis distance between xj, the j-th column of X, and M is defined as , where is the mean computed over the columns of M, and ∑-1 is the inverse covariance matrix associated with M. The objective of a user-based system is to calculate the xj that minimizes the Mahalanobis distance in the sense of least squares. Working with the transpose of M, one obtains an item-based system. To improve computational efficiency, the covariance-based regularizer applies two techniques on dimensionality reduction: matrix factorization and principal component analysis (PCA).

The experiments were conducted on four datasets: FilmTrust, Ciao, MovieLens, and Netflix. The number of ratings in these datasets ranges from tens of thousands to several million. The authors chose the precision and recall scores as metrics of evaluation because their design purpose is not to predict ratings, but to provide a ranked list of items. In experiments, the authors compared the performance of the two techniques on dimensionality reduction and found that PCA is preferable. They further studied two cases of adding constraints in experiments. One of them is to investigate the impact of including neighbors on the precision and recall scores measured with the cosine similarity. The other is to penalize popular items for preserving diversity. As reported, including neighbors may increase both precision and recall scores in almost all cases. However, the improvements are mostly insignificant. The experiments also indicate that penalizing popular items can increase diversity in the recommendations.

The most interesting part of this paper is probably the ways of adding constraints in experiments and the methods of evaluating performance. This can be a good reference for readers working on collaborative filtering.

Reviewer:  Chenyi Hu Review #: CR146525 (1906-0242)
Bookmark and Share
  Featured Reviewer  
 
Data Mining (H.2.8 ... )
 
 
Database Applications (H.2.8 )
 
Would you recommend this review?
yes
no
Other reviews under "Data Mining": Date
Feature selection and effective classifiers
Deogun J. (ed), Choubey S., Raghavan V. (ed), Sever H. (ed) Journal of the American Society for Information Science 49(5): 423-434, 1998. Type: Article
May 1 1999
Rule induction with extension matrices
Wu X. (ed) Journal of the American Society for Information Science 49(5): 435-454, 1998. Type: Article
Jul 1 1998
Predictive data mining
Weiss S., Indurkhya N., Morgan Kaufmann Publishers Inc., San Francisco, CA, 1998. Type: Book (9781558604032)
Feb 1 1999
more...

E-Mail This Printer-Friendly
Send Your Comments
Contact Us
Reproduction in whole or in part without permission is prohibited.   Copyright 1999-2024 ThinkLoud®
Terms of Use
| Privacy Policy