Understanding Distribution Structure on Calibrated Recommendation Systems

--Traditional recommender systems aim to generate a recommendation list comprising the most relevant or similar items to the user's profile. These approaches can create recommendation lists that omit item genres from the less prominent areas of a user's profile, thereby undermining the user's experience. T o solve this problem, the calibrated recommendation system provides a guarantee of including less representative areas in the recommended list. The calibrated context works with three distributions. The first is from the user's profile, the second is from the candidate items, and the last is from the recommendation list. These distributions are G-dimensional, where G is the total number of genres in the system. This high dimensionality requires a different evaluation method, considering that traditional recommenders operate in a one-dimensional data space. In this sense, we implement fifteen models that help to understand how these distributions are structured. We evaluate the users' patterns in three datasets from the movie domain. The results indicate that the models of outlier detection provide a better understanding of the structures. The calibrated system creates recommendation lists that act similarly to traditional recommendation lists, allowing users to change their groups of preferences to the same degree. Commonly, traditional recommender systems generate recommendations with miscalibration [1]. Miscalibration means that the recommendation lists do not follow the user preferences distribution, instead suggesting items from user's dominant area of interest. It creates an overspecialized recommendation list in which the items from the less dominant area are overwhelmed. This effect puts the user in a filter bubble or an echo chamber problem [2]. For instance, when a specific area dominates the recommended list, the user likely has few other options to interact with, aside from items within that dominant area. Then, the subsequent lists are recommended, with the dominant area becoming more overspecialized. In recent years, calibrated recommendation systems have attracted attention [3]-[8] from the recommender system community to overcome this issue. This type of system demonstrates the capacity to improve several objectives, such as diversity [3], control of popularity bias [4], item coverage [5], precision [6], and the reduction of miscalibration [7]. To illustrate how calibrated recommendation works, consider a scenario: if a user's preferences distribution indicates Corresponding author is Diego Corr ˆ ea da Silva.