@conference {812, title = {Persona Finetuning for Online Gaming using Personalisation Techniques}, booktitle = {Proceedings of the 2022 HCII Conference}, year = {2022}, abstract = {

Automatic persona generation has been shown to have specific measurable benefits for application creators and users. In most situations, personas are adequately descriptive and diversified to achieve user type accuracy and coverage. For specific market segments, such as online gaming, using personas may accurately describe existing user base but not changing habit and need that are introduced by the fluidity of the offerings and the delivery methods. Changes in the ways that applications are marketed, such as new payment methods, for example, subscription models, pay-to-play and pay-to-win, payment-driven-gamification, seriously affect user needs and result in direct impact on user acceptance. This work utilises structured user needs from online gaming players to augment personas using personalisation techniques. The personas are finetuned and dediversified to result in concise personas, based on user needs that successfully convey information for creators and users alike.

}, keywords = {collaborative filtering, Data-driven methods, Diversification, Online Gaming, Persona, Personalisation, Usability evaluation, User study}, author = {Dimitris Spiliotopoulos and Dionisis Margaris and Kostas N. Koutrakis and Panos Philippopoulos and Costas Vassilakis} } @article {813, title = {On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets}, journal = {Information}, volume = {13}, year = {2022}, month = {jun}, pages = {302}, abstract = {

The typical goal of a collaborative filtering algorithm is the minimisation of the deviation between rating predictions and factual user ratings so that the recommender system offers suggestions for appropriate items, achieving a higher prediction value. The datasets on which collaborative filtering algorithms are applied vary in terms of sparsity, i.e., regarding the percentage of empty cells in the user{\textendash}item rating matrices. Sparsity is an important factor affecting rating prediction accuracy, since research has proven that collaborative filtering over sparse datasets exhibits a lower accuracy. The present work aims to explore, in a broader context, the factors related to rating prediction accuracy in sparse collaborative filtering datasets, indicating that recommending the items that simply achieve higher prediction values than others, without considering other factors, in some cases, can reduce recommendation accuracy and negatively affect the recommender system{\textquoteright}s success. An extensive evaluation is conducted using sparse collaborative filtering datasets. It is found that the number of near neighbours used for the prediction formulation, the rating average of the user for whom the prediction is generated and the rating average of the item concerning the prediction can indicate, in many cases, whether the rating prediction produced is reliable or not.

}, keywords = {accuracy, collaborative filtering, Evaluation, Personalisation, Rating prediction, Recommender Systems, reliability, Sparse Datasets}, doi = {https://doi.org/10.3390/info13060302}, author = {Dionisis Margaris and Costas Vassilakis and Dimitris Spiliotopoulos} } @conference {786, title = {Policy Making Analysis and Practitioner User Experience}, booktitle = {Proceedings of the 22nd International Conference on Human-Computer Interaction}, year = {2020}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

This article presents the work on social media analysis-driven policy-making platforms that are powered by classic social media analysis technologies, such as policy modelling, linguistic analysis, opinion mining, sentiment analysis and information visualization. The approach examines the user design perspective towards user experience in policymaking for all the innovative modules used. The technology behind such complex task is presented while the resulting platform is appraised on the potential for real world application. The findings drive the development and the requirements for the summative usability assessment tests. We also report on the level the practitioners adopted the policy formulation tools.

}, keywords = {Content Analysis, natural language interfaces, opinion mining, policy making, social network analysis, User Experience}, doi = {10.1007/978-3-030-60114-0_29}, author = {Dimitris Koryzis and Fotios Fitsilis and Dimitris Spiliotopoulos and Theocharis Theocharopoulos and Dionisis Margaris and Costas Vassilakis} } @conference {marg2016-ssci, title = {Pruning and Aging for User Histories in Collaborative Filtering}, booktitle = {Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence}, year = {2016}, month = {12}, abstract = {In this paper, we introduce algorithms for pruning and aging user ratings in collaborative filtering systems, based on their oldness, under the rationale that aged user ratings may not accurately reflect the current state of users regarding their preferences. The aging algorithm reduces the importance of aged ratings, while the pruning algorithm removes them from the database. The algorithms are evaluated against various types of datasets. The pruning algorithm has been found to present a number of advantages, namely (1) reducing the rating database size, (2) achieving better prediction generation times and (3) improving prediction quality by cutting off predictions with high error. The algorithm can be used in all rating databases that include a timestamp and has been proved to be effective in any type of dataset, from movies and music, to videogames and books.}, doi = {10.1109/ssci.2016.7849920}, url = {https://ieeexplore.ieee.org/document/7849920/}, author = {Dionisis Margaris and Costas Vassilakis} }