Supervised Information Retrieval Algorithm

A Supervised Information Retrieval Algorithm is an information retrieval algorithm that is a learning-to-rank algorithm and can be implemented by a supervised IR system (to solve a supervised IR task).

• Context:
  • It can range from being a Pointwise IR Algorithm to being a Pairwise IR Algorithm to being a List-based IR Algorithm.
• See: Data Driven Recommendations Task, Learning to Rank Algorithm.

---

---

References

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. To overcome this bias problem, we present a counterfactual inference framework that provides the theoretical basis for unbiased LTR via Empirical Risk Minimization despite biased data.

2011

• (Li, 2011) ⇒ Hang Li. (2011). “Learning to Rank for Information Retrieval and Natural Language Processing.” Morgan & Claypool Publishers. ISBN:1608457079, 9781608457076 doi:10.2200/S00607ED2V01Y201410HLT026
  • QUOTE: Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on the problem recently and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. ...

2009

• (Liu, 2009) ⇒ Tie-Yan Liu. (2009). “Learning to Rank for Information Retrieval.” In: Foundations and Trends in Information Retrieval Journal, 3(3). [http://dx.doi.org/10.1561/1500000016 doi:10.1561/1500000016
  • QUOTE: Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by using learning-to-rank techniques. The objective of this tutorial is to give an introduction to this research direction. Specifically, the existing learning-to-rank algorithms are reviewed and categorized into three approaches: the pointwise, pairwise, and listwise approaches.

---