Leveraging User Personality and Tag Information for One Class Collaborative Filtering

Sun, Jun; Ren, Deyuan; Xu, Dong

doi:10.1007/978-3-030-00776-8_76

Cited by 3 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first limitation is the self-bias problem [21], when the subjects tend to give the wrong answer to some of the undesired social characteristics in certain circumstances. For example, when answering a recruitment personality question- Item count [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35] TIPI 10…”

Section: A Personality Measurementmentioning

confidence: 99%

“…Recommendation method Publication Personality neighborhood [80], [93], [94], [82], [57], [83], [84], [47], [32], [41], [26], [86], [80], [88], [89] [95], [96], [31] Matrix factorization [30], [59], [97], [98], [99], [25], [34], [100],…”

Section: Table Vi: Personality-aware Recommendation Techniques Classi...mentioning

confidence: 99%

“…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%

See 2 more Smart Citations

A Survey on Personality-Aware Recommendation Systems

Dhelim,

Aung,

Bouras

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract