Predicting behavior

Abstract

Behavior prediction has become an important area of emphasis, with applications ranging from e-commerce, marketing analytics, and financial forecasting to smart health, security informatics, and crime prevention. However, traditional behavior modeling approaches have shortcomings: heavy reliance on objective, observed data, and a failure to consider the granular, micro-level decisions and actions that collectively drive macro-level behavior. To address these shortcomings, the authors present a behavior prediction framework that advocates the integration of objective and perceptual information and decomposes behavior into a series of closely interrelated stages to facilitate enhanced behavior prediction performance. The utility of the framework is demonstrated through a series of experiments pertaining to prediction of auction fraud, e-commerce conversions, and customer churn.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 31 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.