Conflicts of interest often arise between data sources and their users regarding how the users’ information needs should be interpreted by the data source. For example, an online product search might be biased towards presenting certain products higher than in its list of results to manipulate users into buying expensive products and improve its revenue, which may not follow the user’s desired ranking expressed in their query. The research community has proposed schemes for data systems to implement to ensure unbiased results and remove manipulative information. However, data systems and services usually have little or no incentive to implement these measures, e.g., these biases often increase their profits. In this paper, we propose a novel formal framework for querying in settings where the data source has incentives to return biased answers intentionally due to the conflict of interest between the user and the data source. We propose efficient algorithms to detect whether it is possible for users to extract relevant information from manipulative data sources. We propose methods to detect biased information in the results of a query efficiently. We also propose algorithms to reformulate input queries to increase the amount of relevant information in the returned results over manipulative data sources. Using experiments on real-world datasets, we show that our algorithms are efficient and return relevant information over large data.

This framework implements three algorithms from your research paper:

1. Algorithm 1: Detecting Trustworthy Answers
2. Algorithm 2-3: Detecting Influential Queries
3. Algorithm 4: Maximally Informative Query (q★)

pip install -r requirements.txt

python detect-trustworthy-answers.py

Datasets are in \data\real directory. You can also use datasets by placing them in the same directory and updating the file paths in the code. Due to size Amazon dataset is not included in the repository. You can download it from Amazon Product Data.