Wisdom from the crowd: Can recommender systems predict employee turnover and its destinations?

Large Scale Online Multiple Kernel Regression with Application to Time-Series Prediction

(2019) Doyen Sahoo et al.   ACM Transactions on Knowledge Discovery from Data

How AI can be a force for good:

(2018) Mariarosaria Taddeo et al.   SCIENCE

Machine Learning Capabilities of a Simulated Cerebellum

(2017) Matthew Hausknecht et al.   IEEE Transactions on Neural Networks and Learning Systems

Ten simple rules for responsible big data research

(2017) Matthew Zook et al.   PLoS Computational Biology

An Efficient Similarity Measure for User-Based Collaborative Filtering Recommender Systems Inspired by the Physical Resonance Principle

(2017) Zhenhua Tan et al.   IEEE Access

A Century of Selection

(2013) Ann Marie Ryan et al.   Annual Review of Psychology

Genetic and phenotypic relationships between carbohydrate larval antigen (CarLA) IgA, parasite resistance and productivity in serial samples taken from lambs after weaning

(2013) R.J. Shaw et al.   INTERNATIONAL JOURNAL FOR PARASITOLOGY

Sometimes pursuits don't pan out: Anticipated destinations and other caveats: Comment on Hom, Mitchell, Lee, and Griffeth (2012).

(2012) Mindy E. Bergman et al.   PSYCHOLOGICAL BULLETIN

Reviewing employee turnover: Focusing on proximal withdrawal states and an expanded criterion.

(2012) Peter W. Hom et al.   PSYCHOLOGICAL BULLETIN

Improving collaborative filtering recommender system results and performance using genetic algorithms

(2011) Jesus Bobadilla et al.   KNOWLEDGE-BASED SYSTEMS

A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition

(2010) Ana Belén Barragáns-Martínez et al.   INFORMATION SCIENCES