Figure 1: Overview of VINS, our proposed image retrieval process for the visual search for mobile interface design. First, it takes as input UI layout screens, either a complete design or an abstract wireframe. Then, it employs an object detection model to detect the presence and location of the different UI components defining the input query and produces a segmented layout accordingly. This segmented layout is passed to a multi-modal embedding network that learns a joint feature representation of both visual and label features. This representation is used to retrieve a ranked list of similar designs.
Player modeling is an important concept that has gained much attention in game research due to its utility in developing adaptive techniques to target better designs for engagement and retention. Previous work has explored modeling individual differences using machine learning algorithms performed on aggregated game actions. However, players’ individual differences may be better manifested through sequential patterns of the in-game player’s actions. While few works have explored sequential analysis of player data, none have explored the use of Hidden Markov Models (HMM) to model individual differences, which is the topic of this paper. In particular, we developed a modeling approach using data collected from players playing a Role-Playing Game (RPG). Our proposed approach is two fold: 1. We present a Hidden Markov Model (HMM) of player in-game behaviors to model individual differences, and 2. using the output of the HMM, we generate behavioral features used to classify real world players’ characteristics, including game expertise and the big five personality traits. Our results show predictive power for some of personality traits, such as game expertise and conscientiousness, but the most influential factor was game expertise.
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