Text Mining on Player Personality for Game Recommendation

Yang, Hsin-Chang; Lin, Cathy S.; Huang, Zi-Rui; Tsai, Tsung-Hsing

doi:10.1145/3092090.3092132

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…Table VIII summarizes the works that have used these datasets. [34], [95], [166], [87], [100], [148], [161], [106] N/A MovieLens [167], [40], [85], [96], [140], [32], [149], [31], [93] Movies Newsfullness [33], [81], [105] News articles Twitter [145], [161], [114], [116], [115] Friendships IMDB [139], [28], [145] Movies Last.fm [87], [148], [151], [153] Music Other datasets allrecipes.com [168], Amazon [169], Steam [163], [86] ADS [54], [56], Douban [80], [143], [170], IAPS [36], Facebook [58], , PsychoFlickr [55],…”

Section: Newsfullness Datasetmentioning

confidence: 99%

A Survey on Personality-Aware Recommendation Systems

Dhelim,

Aung,

Bouras

et al. 2021

Preprint

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With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of personality-aware recommendation systems. Unlike conventional recommendation systems, these new systems solve traditional problems such as the cold start and data sparsity problems. This survey aims to study and systematically classify personalityaware recommendation systems. To the best of our knowledge, this survey is the first that focuses on personality-aware recommendation systems. We explore the different design choices of personality-aware recommendation systems, by comparing their personality modeling methods, as well as their recommendation techniques. Furthermore, we present the commonly used datasets and point out some of the challenges of personality-aware recommendation systems.

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Section: Newsfullness Datasetmentioning

confidence: 99%

A Survey on Personality-Aware Recommendation Systems

Dhelim,

Aung,

Bouras

et al. 2021

Preprint

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“…Personality extraction through the gaming platform Steam is performed in [Yang et al, 2017], with the goal of better recommending games to users. To do so, they take as input the textual reviews written by the users, as well as the the tags they give to games.…”

Section: Using Personality Extractionmentioning

confidence: 99%

Personality Extraction Through LinkedIn

Piedboeuf

Langlais

Bourg³

2019

Advances in Artificial Intelligence

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Personality extraction through social networks is a field that only recently started to capture the attention of researchers. The task consists in, starting with a corpus of user profiles on a particular social network, classifying their personalities correctly, according to a specific personality model as described in psychology. In this master thesis, three innovations to the domain are presented. Firstly, the collection of a corpus of LinkedIn users. Secondly, the extraction of the personality according to two personality models, DiSC and MBTI, the extraction with DiSC having never been done before. Lastly, the idea of going from one personality model to the other is explored, thus creating the possibility of having the results on two personality models with only one personality test.

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