Abstract
In this paper, we propose a framework that utilizes the concept of a social network for the targeted advertising of products. This approach discovers the cohesive subgroups from a customer's social network as derived from the customer's interaction data, and uses them to infer the probability of a customer preferring a product category from transaction records. This information is then used to construct a targeted advertising system. We evaluate the proposed approach by using both synthetic data and real-world data. The experimental results show that our approach does well at recommending relevant products.
Original language | English |
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Pages (from-to) | 2029-2038 |
Number of pages | 10 |
Journal | Expert Systems with Applications |
Volume | 34 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2008 Apr 1 |
All Science Journal Classification (ASJC) codes
- Engineering(all)
- Computer Science Applications
- Artificial Intelligence