# Boosting the Learning for Ranking Patterns
## 2 Related Work  
Iterative/user-centric pattern mining has gained significant attention in recent years, where the user is involved in the mining process to improve the mining results by providing feedback. This approach is particularly useful in domains such as medical diagnosis or fraud detection, where the user's domain knowledge is essential. To make this process more efficient and effective, researchers have been exploring methods to learn ranking functions that can quickly present patterns to the user based on their interests. This problem, known as object ranking, has been studied extensively in machine learning and data mining communities. One common approach to object ranking is to learn a ranking function based on pairwise comparisons. However, these methods can be time-consuming and expensive since they require a large number of pair-wise comparisons to learn a reliable ranking function. To address this challenge, researchers have explored alternative methods such as multi-criteria decision making. In our approach, we use the analytical hierarchy process (AHP) to learn the measures' weighting vector, which maximizes the correlation between the user's ranking function and the learned AHP-based ranking function. In [6], the authors proposed a user-centric approach to mining interesting patterns based on the user's feedback. They developed a log-linear model for itemsets that considers the user's prior knowledge about the items and their relationships. The model was trained using RankingSVM to learn a ranking function that assigns a score to each itemset. The authors also proposed a belief model that exploits belief probabilities assigned to transactions for more complex patterns. The authors demonstrated that their approach outperformed existing methods in terms of accuracy and efficiency for complex patterns where traditional methods may fail to capture the user's preferences. [PERSON] et al. [3] extended the approach proposed by [PERSON] et al. [6] and used RankingSVM for active preference learning to rank patterns. However, these methods suffer from computational inefficiencies, which limit their scalability on large datasets. In contrast, the AHP-based approach proposed in our work has a linear time complexity and can efficiently rank a large number of patterns. In [14], the authors proposed a user-centric generic framework (PRIIME) to learn ranking functions. To achieve this, regression techniques based on neural networks were adopted. They assigned a score to each pattern based on user feedback and trained a regression model on labeled patterns and their corresponding scores. The model predicts the score of new patterns based on their features and user feedback. The proposed method outperformed existing methods in terms of accuracy and efficiency and can effectively mine interesting patterns and provide valuable insights to the user. In their study, [PERSON] and colleagues [15] investigated changes in the citation patterns of academics in response to the \"evaluation environment\" that emerged in academia with the rise of the World University Rankings (WURs). The authors analyzed papers published in two higher education journals across three periods: pre-WURs (1990-2003), WURs implementation (2004-2010), and adaptation to WURs (2011-2017). To compare and rank first-citation speeds across these periods, they used the non-parametric Kaplan-Meier method [16]. In [17], the authors investigated the factors that influence website rankings of search engine result pages, highlighting the top contributors to better rankings. The study is divided into two parts: a literature review and empirical research. The literature review identified 24 factors that affect website rankings, with the most common being backlinks, social media support, keywords in the title tag, website structure, size, loading time, domain age, and keyword density. In the empirical research, the authors analyzed the top 15 Google results for three search phrases, taking into account the factors identified in the literature review.