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India's privatisation era is always praised for its capacity to create opportunities and more effective business models to support growth. By excluding the weaker, less skilled and more vulnerable groups in society, private enterprises may also be more likely to exacerbate economic imbalances and inequality, according to the current study. Recent data show that inequality in India has significantly increased in a variety of ways. Additionally, it has been asserted that the private sector makes the wealth gaps worse. In a similar vein, most people would only have limited access to a premium knowledge base or service. This is a worry since the government began disinvesting by selling public sector firms to the private sector, which resulted in a progressive decline in State ownership and control over resources. Privatisation results in the State's loss of control over decision-making and price setting. This may increase the likelihood that expensive, high-quality items and services will be. This study makes an effort to offer solid proof of how the private sector contributes to the country's unequal wealth distribution and low levels of knowledge exchange. This study will also explore if the Indian government can reduce income inequality and poverty rates by enacting sound policies that apply to both the public and private sectors. The results would encourage changes in policy aimed at reducing economic inequality in India and advancing welfare.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.