A CBR-Based Game Recommender for Rehabilitation Videogames in Social Networks

Català, Laura; Julián, Vicente; Gil-Gómez, José-Antonio

doi:10.1007/978-3-319-10840-7_45

Cited by 9 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study included comparisons to kNN (cosine) trained on the latent SVD factors, the standard kNN with somewhat small neighbourhood sizes, and random or most popular recommendations. The third study [21] investigated a new case-based disability rehabilitation recommender, which is a type of content recommender. The content was based on game descriptions combined with social network information and questionnaire answers of the users.…”

Section: Related Workmentioning

confidence: 99%

Content Based Player and Game Interaction Model for Game Recommendation in the Cold Start setting

Viljanen,

Vahlo,

Koponen

et al. 2020

Preprint

View full text Add to dashboard Cite

Game recommendation is an important application of recommender systems. Recommendations are made possible by data sets of historical player and game interactions, and sometimes the data sets include features that describe games or players. Collaborative filtering has been found to be the most accurate predictor of past interactions. However, it can only be applied to predict new interactions for those games and players where a significant number of past interactions are present. In other words, predictions for completely new games and players is not possible. In this paper, we use a survey data set of game likes to present content based interaction models that generalize into new games, new players, and both new games and players simultaneously. We find that the models outperform collaborative filtering in these tasks, which makes them useful for real world game recommendation. The content models also provide interpretations of why certain games are liked by certain players for game analytics purposes.

show abstract

Section: Related Workmentioning

confidence: 99%

Content Based Player and Game Interaction Model for Game Recommendation in the Cold Start setting

Viljanen,

Vahlo,

Koponen

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In 2015, Mahmoud et al presented a recommender system for rehabilitation of people with disabilities, specifically for spinal cord injuries [30]. Catalá et al 2014, developed EDIT, a recommender system of games for disabled and elderly people [31]. EDIT collects information from patients and shows them a list of rehabilitation games appropriate to their disability.…”

Section: Related Workmentioning

confidence: 99%

Towards a Smart System for Rehabilitation: TANGO-H Recommender System

González-González¹,

Pa²,

Muñoz-Cruz³

et al. 2018

Preprint

View full text Add to dashboard Cite

The main objective of this work was to build an intelligent system for user rehabilitation. The system comprehends a platform with an exercise player and a designer tool. The intelligent platform analyzes the user interactions with it at every time along with the user record evolution to recommend new activities to the user. The main contributions of this work are, firstly, the definition of a recommender system based on different difficulty levels and user skills. The recommender system therefore has the function of providing the user a personalized game mode based on his own records and preferences. Secondly, a gamified system has been developed in the platform to increase the motivation of users and to prevent them from abandoning. Lastly, a twofold validation of the built system has been performed, with a group of experts in the first place and in a real intervention with children afterwards.

show abstract

“…At present, CBR has been widely used in AI, and it has become a new methodology of problem solving and learning [10]. With the gradual maturity of theories and methods, the applications of CBR have been extended to various fields, including medical treatment [11,12,13,14,15], planning [16,17], assessment [18,19], forecast [20,21], game [22], recommendation system [23], management [24] and so on [25,26].…”

Section: Introductionmentioning

confidence: 99%

A hybrid retrieval strategy for case-based reasoning using soft likelihood functions

et al. 2022

View full text Add to dashboard Cite

Case-based reasoning (CBR) is the retrieval of one or more similar cases from an existing case base for the problem to be solved according to the characteristics of the new problem. The core idea of CBR is that similar cases have similar solutions, so whether the CBR system can play a powerful advantage depends on the quality of case retrieval strategy. At present, the commonly used case retrieval algorithm is based on the mean operator method, which is very hard, and a certain local similarity is low will affect the overall result. In order to calculate the global similarity of cases from a new and softer point of view, this paper introduces the soft likelihood functions into case retrieval, combines the soft likelihood functions with KNN, and proposes a hybrid retrieval strategy.The core of the retrieval strategy is to define the global similarity through SLFs, aggregate the local similarity and characteristic similarity together, and also take the attitude characteristics of decision makers into consideration. Through simulation experiments on real data sets, the accuracy rate is more than 81%, which verifies the effectiveness of the retrieval strategy.

show abstract

A CBR-Based Game Recommender for Rehabilitation Videogames in Social Networks

Cited by 9 publications

References 17 publications

Content Based Player and Game Interaction Model for Game Recommendation in the Cold Start setting

Content Based Player and Game Interaction Model for Game Recommendation in the Cold Start setting

Towards a Smart System for Rehabilitation: TANGO-H Recommender System

A hybrid retrieval strategy for case-based reasoning using soft likelihood functions

Contact Info

Product

Resources

About