scholarly journals A Cooperative Coevolution Framework for Parallel Learning to Rank

2015 ◽  
Vol 27 (12) ◽  
pp. 3152-3165 ◽  
Author(s):  
Shuaiqiang Wang ◽  
Yun Wu ◽  
Byron J. Gao ◽  
Ke Wang ◽  
Hady W. Lauw ◽  
...  
Keyword(s):  
Learning To Rank ◽  
Cooperative Coevolution ◽  
Parallel Learning

A Modification of Daily Learning Flow in Inclusive Education Programs

CENDEKIAWAN ◽  
2019 ◽  
Vol 1 (2) ◽  
pp. 10-15
Author(s):  
RIzka Harfiani ◽  
Hasrian Rudi Setiawan
Keyword(s):  
Learning Outcomes ◽  
Inclusive Education ◽  
Education Programs ◽  
Attention Span ◽  
Parallel Learning ◽  
Learning Flow

Pendidikan inklusif kini menjadi fokus perhatian dalam upaya pemberian layanan pendidikan bagi semua anak, termasuk anak berkebutuhan khusus. Berbagai permasalahan kerap dijumpai dalam proses pembelajaran di kelas inklusif, untuk itu penelitian ini bertujuan menganalisis modifikasi alur pembelajaran harian pendidikan inklusif di Raudhatul Athfal An-Nahl, Jakarta. Penelitian ini menggunakan pendekatan kualitatif dengan jenis penelitian studi kasus. Teknik pengumpulan data yang digunakan adalah observasi, wawancara, dan dokumentasi. Teknis analisis data menggunakan model analisis interaktif Miles and Huberman, serta pengujian keabsahan data dengan metode triangulasi. Hasil penelitian menemukan modifikasi alur pembelajaran harian di RA. An-Nahl terdiri dari pre-opener, opener, energizer, activity, linking dan summeryzing, review, mission, dan closer. Hal yang perlu diperhatikan dalam proses pembelajaran adalah engagement, attention span, readiness, activity, reviewing, learning outcomes dan parallel learning outcomes. Kesimpulan dari penelitian ini menunjukkan bahwa modifikasi alur pembelajaran harian mampu mengakomodir kelebihan maupun kelemahan sesuai karakter masing-masing siswa, serta mampu mengatasi permasalahan dalam proses pembelajaran di kelas inklusif.

Learning to Rank an Assortment of Products

2019 ◽  
Author(s):  
Kris Ferreira ◽  
Sunanda Parthasarathy ◽  
Shreyas Sekar
Keyword(s):  
Learning To Rank

Unbiased Learning to Rank

2021 ◽  
Vol 39 (2) ◽  
pp. 1-29
Author(s):  
Qingyao Ai ◽  
Tao Yang ◽  
Huazheng Wang ◽  
Jiaxin Mao
Keyword(s):  
Online Learning ◽  
Theoretical Foundation ◽  
Learning To Rank ◽  
Research Question ◽  
Parameters Estimation ◽  
User Interactions ◽  
Empirical Performance ◽  
Search Data ◽  
Two Sides ◽  
Important Research Question

How to obtain an unbiased ranking model by learning to rank with biased user feedback is an important research question for IR. Existing work on unbiased learning to rank (ULTR) can be broadly categorized into two groups—the studies on unbiased learning algorithms with logged data, namely, the offline unbiased learning, and the studies on unbiased parameters estimation with real-time user interactions, namely, the online learning to rank. While their definitions of unbiasness are different, these two types of ULTR algorithms share the same goal—to find the best models that rank documents based on their intrinsic relevance or utility. However, most studies on offline and online unbiased learning to rank are carried in parallel without detailed comparisons on their background theories and empirical performance. In this article, we formalize the task of unbiased learning to rank and show that existing algorithms for offline unbiased learning and online learning to rank are just the two sides of the same coin. We evaluate eight state-of-the-art ULTR algorithms and find that many of them can be used in both offline settings and online environments with or without minor modifications. Further, we analyze how different offline and online learning paradigms would affect the theoretical foundation and empirical effectiveness of each algorithm on both synthetic and real search data. Our findings provide important insights and guidelines for choosing and deploying ULTR algorithms in practice.

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