A game recommender system using collaborative filtering (GAMBIT)

Anwar, Syed Muhammad; Talha, Shahzad; Sattar, Zunaira; Khan, Rahma; Majid, Muhammad

doi:10.1109/ibcast.2017.7868073

Cited by 21 publications

(10 citation statements)

References 2 publications

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“…For example, while one shotgun may be used by a slight majority of top tier players, another shotgun may be just as deadly in the hands of slightly different, but indistinguishable to the algorithm, players. For this reason, calculating loss off of the recommendations would be next to impossible [23], [16], [17], [15], [18]. For this reason, an evaluation via a user study as defined by Shani and Gunawardana [23] was instead performed on real Destiny players (a similar general approach also adopted by Anwar et al [18]).…”

Section: Evaluation and Resultsmentioning

confidence: 99%

“…The work focused on recommending games, similar to movie recommendations on platforms such as Netflix or app recommendations on the AppStore [16], [17]. Similarly, Anwar et al [18] used collaborative filtering to suggest games to players via evaluating the opinions of similar players. Notably, the system was evaluated via a live player sample, an approach that is also adopted here.…”

Section: Related Workmentioning

confidence: 99%

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Controlling the crucible

Sifa

Pawlakos²,

Zhai³

et al. 2018

Proceedings of the Australasian Computer Science Week Multiconference

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Section: Evaluation and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Controlling the crucible

Sifa

Pawlakos²,

Zhai³

et al. 2018

Proceedings of the Australasian Computer Science Week Multiconference

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“…The courses were classified as either relevant or not relevant and recommended or not recommended. Contingency and confusion matrices were adopted in this experiment owing to their capabilities to validate accuracy [23]. The proposed model made 214 correct predictions and 21 incorrect predictions in the confusion matrix.…”

Section: B Validate the Accuracy Of The Course Recommendation By Thementioning

confidence: 99%

A Proposed Course Recommender Model based on Collaborative Filtering for Course Registration

Salehudin¹,

Kahtan²,

Abdulgabber³

et al. 2019

IJACSA

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Students face issues and challenges in making decisions for course registration. Traditionally, students rely on suggestions from academic advisers prior to course registration. Therefore, students spend a considerable amount of time waiting for advisers to help them register for the right subjects. However, the number of students rises yearly, thereby increasing the responsibilities of lecturers. Moreover, academic advisers experience constraints in analysing data during consultations for course registration. Therefore, this study proposes a course recommender model based on collaborative filtering. Collaborative filtering is adopted because it provides recommendations based on students' performance in previous subjects. A dataset from the Information & Communication Technology Centre (ICT) of the University Malaysia Pahang is used to evaluate the proposed model. The evaluation is conducted based on two experiments. The first experiment is performed by calculating the difference between actual and predicted scores to verify prediction accuracy. Results show that the average of the mean absolute error of the proposed model is 0.319, which is highly accurate. The second experiment is conducted by comparing the recommendations of the proposed model with those of experts to validate the course recommendation accuracy of the proposed model. Results of the second experiment show that the proposed model has a 91.06% accuracy rate with an error rate of 8.94%. In addition, average precision is 0.68 and recall is 0.724, which are considered accurate. Therefore, the proposed model can play a vital role in assisting students and academic advisers to recommend the right courses during registration, thereby overcoming the limitations of academic advising.

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“…In addition, existing game recommendation systems were developed for gamers who played a variety of games. In this case, to analyze a player's propensity to play, the system used "collaborative filtering", a recommendation system based on the gaming experience of the user [3]. In this process, additional reviews were utilized to encourage users to review and evaluate games based on additional criteria.…”

Section: Introductionmentioning

confidence: 99%

A Mapping Approach to Identify Player Types for Game Recommendations

Lee

Jung

2019

Information

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As the size of the domestic and international gaming industry gradually grows, various games are undergoing rapid development cycles to compete in the current market. However, selecting and recommending suitable games for users continues to be a challenging problem. Although game recommendation systems based on the prior gaming experience of users exist, they are limited owing to the cold start problem. Unlike existing approaches, the current study addressed existing problems by identifying the personality of the user through a personality diagnostic test and mapping the personality to the player type. In addition, an Android app-based prototype was developed that recommends games by mapping tag information about the user’s personality and the game. A set of user experiments were conducted to verify the feasibility of the proposed mapping model and the recommendation prototype.

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A game recommender system using collaborative filtering (GAMBIT)

Cited by 21 publications

References 2 publications

Controlling the crucible

Controlling the crucible

A Proposed Course Recommender Model based on Collaborative Filtering for Course Registration

A Mapping Approach to Identify Player Types for Game Recommendations

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