Pith. Sign in

RECORD

Bayesian Non-parametric model to Target Gamification Notifications Using Big Data

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
Meisam Hejazi Nia, Brian Ratchford

Artificial Intelligence · Submitted 4 Nov 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim I suggest an approach that helps the online marketers to target their Gamification elements to users by modifying the order of the list of tasks that they send to users.

how it is stated · hedged possibility 0.94 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID1611.02154 (version 1)
Pith IDFBZGTKAQ, Pith's permanent record of this paper and its review
CitationBrian Ratchford, Meisam Hejazi Nia. "Bayesian Non-parametric model to Target Gamification Notifications Using Big Data." arXiv:1611.02154 (2016). https://pith.science/paper/FBZGTKAQ
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,878 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.08 · human participants 0.36 · controlled comparison 0.04 · randomized assignment 0.02 · states quantitative result 0.03 · compares with prior results 0.05 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

I suggest an approach that helps the online marketers to target their Gamification elements to users by modifying the order of the list of tasks that they send to users. It is more realistic and flexible as it allows the model to learn more parameters when the online marketers collect more data. The targeting approach is scalable and quick, and it can be used over streaming data.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 I suggest an approach that helps the online marketers to target their Gamification elements to users by modifying the order of the list of tasks that they send to users. contribution 0.89 0.53 0.42 hedged possibility 0.94
s1 It is more realistic and flexible as it allows the model to learn more parameters when the online marketers collect more data. contribution 0.56 0.34 0.10 asserted 0.99
s2 The targeting approach is scalable and quick, and it can be used over streaming data. contribution 0.60 0.47 0.16 asserted 0.93

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from argument 0.95.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

I suggest an approach that helps the online marketers to target their Gamification elements to users by modifying the order of the list of tasks that they send to users. It is more realistic and flexible as it allows the model to learn more parameters when the online marketers collect more data. The targeting approach is scalable and quick, and it can be used over streaming data.

Human review

How Pith checks papers → · Full audit of this paper →

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.