Abstract
Nowadays, the users of a web platform, such as a video-on-demand service or an eCommerce site, are routinely using the platform’s recommender system (RS) when choosing which item to consume or buy (e.g. movies or books). It is therefore important to understand how the exposure to recommendations can influence the users’ choices, particularly the quality and distribution of the chosen items. However, users, even in the presence of the same RS, may show diverse and even atypical choice behaviours, which are independent of the RS; they may have a preference for choosing more popular or recent items. The effect of these behaviours on the collective evolution of the choices and the performance of the RS is not well-understood yet. In fact, in previous analyses, the users were supposed to only choose among the top recommendations, without any further discrimination. Hence, we first perform a correlation analysis, in some choice data sets, revealing that three kinds of choice behaviours, namely the tendency to choose popular, recent, and highly rated items, are actually observable in large percentages of the users. Then, we investigate how these choice behaviours, implemented as algorithmic choice models (Popularity-CM, Age-CM and Rating-CM), can influence the overall choice distribution and performance of the RS. With the aim of understanding such relationships and consequences, we have designed a simulation framework where the considered choice models (CMs) are adopted to simulate users’ choices when they are exposed to recommendations from alternative RSs. We found that (1) the choices’ distribution of a user population is significantly influenced not only by the RS, but also by the prevalent choice model of the population, (2) RS have some effects on the choices that are independent of the adoption of the CM, and (3) some important effects of the RS on users’ choice distribution depend also on the choice model that the users adopt. The study contributes to the start of a new line of research where the impact of recommendation technologies can be studied with respect to alternative decision-making approaches, which are actually followed by real users. Additionally, the simulation approach can help other researchers and practitioners to investigate the effect of deploying an RS when a certain CM is identified in a population of users.