Mining target users for mobile advertising based on telecom big data

Abstract

The mobile advertising industry in China has developed rapidly in recent years. Many companies and brands tend to employ mobile advertising in order to reach the target customers accurately. However, the conversion rates associated with the advertising campaigns are usually quite low due to the low quality of the datasets and impropriate predictive model. In this paper, we propose a novel mobile advertising system architecture based on telecom big data analytics. The defined multi-dimensional user portrait is introduced for user label oriented ad display strategy or as the basic database for the further user classification algorithm. We also adopt the widely used logistic regression algorithm in this paper to improve the target accuracy. The result of use case, which is calculated from the real-time collected cellular network data, also shows the superior performance of the proposed mobile advertising system.

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 — 13 citations exist upstream.

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