Machine Learning Methods for Interactive Search Interfaces and Cognitive Models
Antti Kangasrääsiö · 2018
Computer systems that users interact with are becoming more and more driven by artificial intelligence and machine learning components. This means that the ability of the users to efficiently interact with these intelligent systems on one hand, and the ability of these intelligent systems to understand the users on the other hand, are becoming more and more important for productive human-computer interaction. This thesis proposes new methods to improve both of these aspects. The first contribution of this thesis is to improve the ability of the users to predict the consequences of their actions, and to observe possible inconsistencies in the feedback they give, when interacting with an information retrieval system that performs interactive user modelling. The proposed solutions for improving predictability are interactive visualization of the consequences of user actions and changing the behavior of the user model to better match user expectations. The proposed solutions for detecting inconsistencies in user feedback are visualization of past user feedback and interactive modelling of the accuracy of the feedback. Experiments demonstrate that the proposed methods improve user satisfaction and the usability of the search system. The second contribution is to develop generally applicable methods for inferring the parameter values for various types of models of the user's cognition. The inherent difficulty in estimating these parameter values is caused by the complicated relation between the parameters of these cognitive models and the observation data: the likelihood function. The proposed solution is to use likelihood-free Bayesian inference, which is applicable for various different cognitive models and also able to quantify the uncertainty of the parameter estimates. Experiments demonstrate that the proposed solution enables efficient inference of cognitive model parameter values in multiple settings, and also allows informative quantification of parameter uncertainty across the parameter space.; Interaktiiviset tietokonejarjestelmat sisaltavat enenevissa maarin tekoaly- ja koneoppimis-komponentteja. Naiden jarjestelmien hyodyllisyyden kannalta on tarkea kiinnittaa huomiota toisaalta niiden kaytettavyyteen, ja toisaalta siihen, etta ne kykenevat mallintamaan kayttajiensa tarpeita riittavalla tarkkuudella. Tama vaitostutkimus esittaa keinoja kummankin paamaaran edistamiseksi. Tutkimuksen ensimmaisessa osiossa esitetaan menetelmia, joilla parannetaan interaktiivisen hakukoneen kaytettavyytta lisaamalla interaktion ennustettavuutta seka kayttajan epajohdon-mukaisen palautteen tunnistamista. Ennustettavuutta parannettiin seka interaktiivisesti visualisoimalla kayttajan toimintojen mahdollisia vaikutuksia etta muuttamalla kayttajamallin dynamiikkaa vastaamaan paremmin kayttajan odotuksia. Epajohdonmukaisen palautteen maaraa vahennettiin seka visualisoimalla kayttajan antamaa palautetta kokonaisuutena etta mallintamalla interaktiivisesti kayttajan antaman palautteen tarkkuutta. Kokeellisesti naytettiin, etta…