Researcher spanning industrial recommendation and spoken dialogue: generative ranking and full-duplex turn-taking.
2
papers here
2
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-09-20) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
No publication-count data is available in this snapshot, so output is unscored. This axis would measure research productivity: total papers, output over time, and venue types, once a fuller bibliographic record is linked to this name.
Impact—
No citation or influence data is available, so impact is unscored. This axis would measure how widely the work spreads: citation counts, citing literature, and downstream uptake, requiring a linked citation record that this snapshot lacks.
Focus0/100
This axis scores 0 because the underlying field distribution is split exactly 50/50 between recommender systems (machine learning / information retrieval, industrial) and speech and natural language processing (spoken dialogue systems). The zero reflects maximal dispersion across two active lines — one industrial recommendation, one spoken dialogue — rather than an absence of substantial work; concentration could rise as more of the record is added.
Collaboration85/100
Scored 85 on the strength of 71 distinct coauthors, with 12 recurring collaborators including Venkat Srinivas, Chenzhang He, Sam Woodmansee, Shawn Lian, Wenjie Hu, and Renjie Jiang. Both reviewed 2026 papers are multi-author team efforts spanning an industrial recommender-systems group and a spoken-dialogue group, indicating broad cross-community collaboration rather than small-team or solo research.
Activity—
Insufficient data to score activity: the snapshot covers only two reviewed 2026 papers with no year-by-year publication cadence. This axis would measure publishing continuity and recent output rate once a longer publication record is available.
Reproducibility38/100
Scored 38, the mean readiness rating across 2 scored papers. LIGE-GR rates low: its roughly 1% watch-time gain for about 10% compute rests on internal A/B tests, unverifiable outside Meta. ECHO rates medium: it offers a benchmark design, but the review flags label-dependent interruption acoustic processing and the absence of a gain-free or waveform-reuse control.
Research summary
Yang Song (a common name; this profile is name-resolved from public bibliographic data, not biographical records) works across two fields in near-equal measure: recommender systems (machine learning / information retrieval, industrial) and speech and natural language processing (spoken dialogue systems), each accounting for 0.5 of the field distribution in the bibliometric snapshot. The Evolve catalog contains two 2026 arXiv papers. In LIGE-GR, a middle-author contribution, existing rankers are upgraded in place to listwise generative recommendation; Evolve's review reports roughly 1% watch-time lift for about 10% compute, but notes the evidence is internal A/B testing only, unverifiable outside Meta. In ECHO, a last-author contribution, a matched-insertion paired benchmark exposes YIELD bias in full-duplex turn-taking; the review credits the design while questioning label-dependent interruption acoustic processing and the absence of a gain-free or waveform-reuse control. The collaboration footprint is broad: 71 distinct coauthors with 12 recurring collaborators, yielding a collaboration score of 85. Reproducibility scores 38, the mean across 2 scored papers, consistent with low readiness for LIGE-GR and medium for ECHO. The focus score of 0 reflects the even 50/50 field split rather than lack of work. Output, impact, and activity metrics are not yet available in this snapshot.
Collaboration pattern
Collaboration is broad and team-based: the record lists 71 distinct coauthors, 12 of whom recur, including Venkat Srinivas, Chenzhang He, Sam Woodmansee, Shawn Lian, Wenjie Hu, Renjie Jiang, Ziheng Huang, Xinyuan Zhang, Zhihao Zheng, Zhuoran Yu, Rui Li, and Lei Yuan. Both reviewed papers sit in multi-author industrial-style teams, one in recommender systems and one in spoken dialogue, consistent with a collaboration score of 85.
Research keywords
Highlights
LIGE-GR: a smooth leap from ranking to listwise generative recommendation
ECHO: a matched-contrast benchmark for full-duplex turn-taking
Timeline
- 2026Middle author on LIGE-GR, an arXiv paper moving industrial rankers toward listwise generative recommendation in the LLM era.
- 2026Last author on ECHO, an arXiv benchmark of matched-insertion pairs for context-sensitive turn-taking in full-duplex dialogue.
- 2026Reviewed output lands in two fields at once — recommender systems and spoken dialogue systems — matching the 50/50 field split recorded in the bibliometric snapshot.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Bibliometric scores are computed from public bibliographic data only; citation, output, and activity counts were not available in this snapshot.
- "Yang Song" is a common name and no ORCID or institutional affiliation was provided, so this profile is name-resolved from public bibliographic data rather than a verified identity.
- Qualitative claims rest on only 2 papers in the Evolve review catalog; the researcher's full publication record may be substantially larger.
- Radar scores reflect the platform's own bibliometric method, not external rankings; the focus and reproducibility scores describe the current snapshot.