Kristensen et al., 2022 - Google Patents
Personalized game difficulty prediction using factorization machinesKristensen et al., 2022
View PDF- Document ID
- 7804299127988142374
- Author
- Kristensen J
- Guckelsberger C
- Burelli P
- Hämäläinen P
- Publication year
- Publication venue
- Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology
External Links
Snippet
The accurate and personalized estimation of task difficulty provides many opportunities for optimizing user experience. However, user diversity makes such difficulty estimation hard, in that empirical measurements from some user sample do not necessarily generalize to …
- 238000007637 random forest analysis 0 abstract description 37
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
- G06Q30/0202—Market predictions or demand forecasting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2216/00—Indexing scheme relating to additional aspects of information retrieval not explicitly covered by G06F17/30 and subgroups
- G06F2216/03—Data mining
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Drachen et al. | Skill-based differences in spatio-temporal team behaviour in defence of the ancients 2 (dota 2) | |
Chen et al. | Modeling intransitivity in matchup and comparison data | |
Drachen et al. | Rapid prediction of player retention in free-to-play mobile games | |
Canossa | Meaning in gameplay: Filtering variables, defining metrics, extracting features and creating models for gameplay analysis | |
Kristensen et al. | Personalized game difficulty prediction using factorization machines | |
Aung et al. | The trails of just cause 2: spatio-temporal player profiling in open-world games | |
Javvaji et al. | Understanding player patterns by combining knowledge-based data abstraction with interactive visualization | |
Thawonmas et al. | Artificial general intelligence in games: Where play meets design and user experience | |
Li et al. | A multi-phased co-design of an interactive analytics system for moba game occurrences | |
Janusz et al. | Learning multimodal entity representations and their ensembles, with applications in a data-driven advisory framework for video game players | |
Kohwalter et al. | Provchastic: Understanding and predicting game events using provenance | |
Kristensen et al. | Difficulty modelling in mobile puzzle games: An empirical study on different methods to combine player analytics and simulated data | |
Alomari et al. | Predicting success of a mobile game: a proposed data analytics-based prediction model | |
Bunker et al. | Machine learning for soccer match result prediction | |
Adam et al. | Markov-switching decision trees | |
Chen et al. | Improving StarCraft II player league prediction with macro-level features | |
Su | Game analytics research: status and trends | |
Hamdad et al. | Basketball analytics. Data mining for acquiring performances | |
Biemer et al. | Solution Path Heuristics for Predicting Difficulty and Enjoyment Ratings of Roguelike Level Segments | |
Xue | Application of artificial intelligence in digital games based on mathematical statistics | |
Azaria et al. | Evolving artificial general intelligence for video game controllers | |
Jalovaara | Win probability estimation for strategic decision-making in esports | |
Periáñez et al. | Advanced Data Science Models for Player Behavioral Prediction | |
Roque | Esports: Video Game Data Analysis | |
Canizales | Implementation of a Pre-Assessment Module to Improve the Initial Player Experience Using Previous Gaming Information |