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Zihao Wang

14 papers hereh-index 191.6k citations62 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author6
  • middle author6
  • last author2

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • cs.CV6
  • cs.CL4
  • cs.LG2
  • cs.SD1
  • econ.GN1
same name
  • Zihao Wang — 12 papers
  • Zihao Wang — 10 papers
  • Zihao Wang — 7 papers
  • Zihao Wang — 4 papers
  • Zihao Wang — 4 papers, h 5
  • Zihao Wang — 2 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172022
most citedNeural Rating Regression with Abstractive Tips Generation for Recommendation

306 citations · 391 across the 11 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2019

Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion

Zihao Wang, Kwun Ping Lai, Piji Li +2

For large-scale knowledge graphs (KGs), recent research has been focusing on the large proportion of infrequent relations which have been ignored by previous studies. For example f…

cs.CL2019★ 14 cited

Persona-Aware Tips Generation

Piji Li, Zihao Wang, Lidong Bing +1

Tips, as a compacted and concise form of reviews, were paid less attention by researchers. In this paper, we investigate the task of tips generation by considering the `persona' in…

cs.CL2017★ 33 cited

Deep Recurrent Generative Decoder for Abstractive Text Summarization

Piji Li, Wai Lam, Lidong Bing +1

We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN…

cs.CL2017★ 306 cited

Neural Rating Regression with Abstractive Tips Generation for Recommendation

Piji Li, Zihao Wang, Zhaochun Ren +2

Recently, some E-commerce sites launch a new interaction box called Tips on their mobile apps. Users can express their experience and feelings or provide suggestions using short te…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.