# Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User
## 1 Introduction  
Recently, many changes have occurred in the world due to the difficult times we are going through. The pandemic has significantly affected many areas; e-commerce and e-marketing have acquired the largest share. Both consumers' and sellers' behavior has been affected and differed significantly from previously, which prompted many companies to handle this matter by making more efforts to analyze customer interests and desires [1, 2]. The primary motivation is to enhance their marketing capabilities and increase the commercial share, taking into account the increased degree of competition between companies to realize customer satisfaction and increase their loyalty [3, 4]. To this end, companies collect and analyze a lot of essential data to provide marketing and promotional offers that match customers' requirements [5, 6].  
Generally, the term personalization refers to allocating the system's output based on its users' personal information. Web personalization is one of the most significant applications based on analyzing the customers' behavior. Personalization's primary goal is represented in two main objects, realizing a satisfying interaction for customers and vendors and predicting the consumer's needs [1, 6, 7]. Personalization techniques are widelyutilized by many websites, including search engines and online social networks, to enhance their capabilities of providing users with the most relevant information to search queries and preferences [8].  
Most online shopping websites allow customers to submit their feedback about offered products and services. The analysis of customers' data, besides their feedback, could help companies greatly while developing their marketing strategies, product redesign, etc., [9; 10]. Customer gender is considered a significant aspect of personal data that can help understand and predict customer behavior and enhance organizations' capabilities to provide users with the most relevant information to search queries and preferences [11; 12].  
Commonly, gender classification is considered a binary task, \"Male and Female\". However, when dealing with the username or profile name, it may be extended to be a ternary task where some people, for several reasons, prefer to be registered anonymously; up to the end of this paper, the term username and profile name will be used interchangeably. Furthermore, digital platforms may allow users to be registered using virtual genders that enhance physical anonymity [13].  
Gender classification represents a challenging task attracting researchers from various fields, including computer vision, data science, pattern recognition, etc. Automatic gender recognition has a wide range of applications in many fields that justify its importance, including commercial development (i.e., advertising), law enforcement (i.e., legal investigations), human-computer interaction (i.e., intelligent systems), and demographic research (i.e., social classification).  
Several techniques were proposed for gender classification that depend on the type of data in which the technique was applied (e.g., image, speech, text, biometrics) [14; 15; 16; 17; 18; 19]. Our goal is to recognize the gender from text, more specifically, from the username. Most customers prefer to write short feedback, making it insufficient to use text-based approaches for the Gender Identification (GI) task.  
The proposed approach encounters many challenges that make the GI a problematic task. The first challenge is that the username may not contain only alphabetical letters; also, it is possible to accept numbers, symbols, and special characters, making the classification task not straightforward. Second, using the Leet language that users widely employ implies using some predefined numbers instead of letters. Such a situation makes removing numbers a risky task that requires a backward process of substitutions to retrieve the original name. The third comprises using a short name form by ignoring writing one or more middle letters, typically vowels.