Search Ranking Task

A Search Ranking Task is a ranking task to produce search results.

• See: IR Task, User Relevance.

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References

2018

• (Grbovic & Cheng, 2018) ⇒ Mihajlo Grbovic, and Haibin Cheng. (2018). “Real-time Personalization Using Embeddings for Search Ranking at Airbnb.” In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining.
  • QUOTE: ... Search Ranking and Recommendations are fundamental problems of crucial interest to major Internet companies, including web search engines, content publishing websites and marketplaces. However, despite sharing some common characteristics a one-size-fits-all solution does not exist in this space. Given a large difference in content that needs to be ranked, personalized and recommended, each marketplace has a somewhat unique challenge. Correspondingly, at Airbnb, a short-term rental marketplace, search and recommendation problems are quite unique, being a two-sided marketplace in which one needs to optimize for host and guest preferences, in a world where a user rarely consumes the same item twice and one listing can accept only one guest for a certain set of dates. ...

2017

• (Joachims, Swaminathan et al., 2017) ⇒ Thorsten Joachims, Adith Swaminathan, and Tobias Schnabel. (2017). “Unbiased Learning-to-Rank with Biased Feedback.” In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ISBN:978-1-4503-4675-7 doi:10.1145/3018661.3018699
  • QUOTE: Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive to collect, user centric, and timely), its inherent biases are a key obstacle to its effective use. For example, position bias in search rankings strongly influences how many clicks a result receives, so that directly using click data as a training signal in Learning-to-Rank (LTR) methods yields sub-optimal results. ...

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