The proliferation of personalized recommendation technologies has raised concerns about discrepancies in their recommendation performance across different genders, age groups, and racial or ethnic populations. This varying degree of performance could impact users’ trust in the system and may pose legal and ethical issues in domains where fairness and equity are critical concerns, like job recommendation. In this paper, we investigate several potential factors that could be associated with discriminatory performance of a recommendation algorithm for women versus men. We specifically study several characteristics of user profiles and analyze their possible associations with disparate behavior of the system towards different genders. These characteristics include the anomaly in rating behavior, the entropy of users’ profiles, and the users’ profile size. Our experimental results on a public dataset using four recommendation algorithms show that, based on all the three mentioned factors, women get less accurate recommendations than men indicating an unfair nature of recommendation algorithms across genders.

The Like other social systems, in collaborative filtering a small number main tenet of this approach is to recommend items of interest to a of "influential" users may have a large impact on the recommendations user based on the preferences of other similar users in the system. of other users, thus affecting the overall behavior of the Because of the social nature of these systems, a small group of system. Identifying influential users and studying their impact on "influential" users can have a significant impact on the behavior other users is an important problem because it provides insight of the system towards other users. This type of influence may, in into how small groups can inadvertently or intentionally affect the some cases, result in undesirable effects such as bias toward certain behavior of the system as a whole. Modeling these influences can items, lack of diversity or imbalance in recommendations, and even also shed light on patterns and relationships that would otherwise potential security concerns such as making it easier to deliberately be difficult to discern, hopefully leading to more transparency in manipulate the system outcomes.