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Characterising Players of a Cube Puzzle Game with a Two-level Bag of Words
dc.contributor.author | Anadón, Xavier | |
dc.contributor.author | Sanahuja, Pablo | |
dc.contributor.author | Traver, V. Javier | |
dc.contributor.author | Lopez, Angeles | |
dc.contributor.author | Ribelles, Jose | |
dc.date.accessioned | 2021-07-26T11:50:00Z | |
dc.date.available | 2021-07-26T11:50:00Z | |
dc.date.issued | 2021-06 | |
dc.identifier.citation | ANADÓN, Xavier, et al. Characterising Players of a Cube Puzzle Game with a Two-level Bag of Words. In: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. 2021. p. 47-53. | ca_CA |
dc.identifier.isbn | 978-1-4503-8367-7 | |
dc.identifier.uri | http://hdl.handle.net/10234/194287 | |
dc.description | Ponencia presentada en UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, Utrecht (Netherlands), June 21 - 25, 2021 | ca_CA |
dc.description.abstract | This work explores an unsupervised approach for modelling players of a 2D cube puzzle game with the ultimate goal of customising the game for particular players based solely on their interaction data. To that end, user interactions when solving puzzles are coded as images. Then, a feature embedding is learned for each puzzle with a convolutional network trained to regress the players’ comple tion effort in terms of time and number of clicks. Next, the known bag-of-words technique is used at two levels. First, sets of puzzles are represented using the puzzle feature embeddings as the input space. Second, the resulting first-level histograms are used as input space for characterising players. As a result, new players can be characterised in terms of the resulting second-level histograms. Preliminary results indicate that the approach is effective for char acterising players in terms of performance. It is also tentatively observed that other personal perceptions and preferences, beyond performance, are somehow implicitly captured from behavioural data. | ca_CA |
dc.format.extent | 7 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Association for Computing Machinery (ACM) | ca_CA |
dc.relation.isPartOf | Judith Masthoff, Eelco Herder, Nava Tintarev, Marko Tkalčič (Eds.). UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, Association for Computing Machinery, 2021, ISBN 978-1-4503-8367-7 | ca_CA |
dc.rights | © 2021 Copyright held by the owner/author(s | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | videogame | ca_CA |
dc.subject | player characterisation | ca_CA |
dc.subject | performance prediction | ca_CA |
dc.subject | machine learning | ca_CA |
dc.subject | clustering | ca_CA |
dc.subject | bag of words | ca_CA |
dc.title | Characterising Players of a Cube Puzzle Game with a Two-level Bag of Words | ca_CA |
dc.type | info:eu-repo/semantics/conferenceObject | ca_CA |
dc.identifier.doi | https://doi.org/10.1145/3450614.3461690 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |