Name
Title | ||
|---|---|---|
Learning Non-Linear Ranking Functions For Web Search Using Probabilistic Model Building Gp |
Abstract | ||
|---|---|---|
Ranking the set of search results according to their relevance to a user query is an important task in an Information Retrieval (IR) systems such as a Web Search Engine. Learning the optimal ranking function for this task is a challenging problem because one must consider complex non-linear interactions between numerous factors such as the novelty, authority, contextual similarity, etc. of thousands of documents that contain the user query. We model this task as a non-linear ranking problem, for which we propose Rank-PMBGP, an efficient algorithm to learn an optimal non-linear ranking function using Probabilistic Model Building Genetic Programming. We evaluate the proposed method using the LETOR dataset, a standard benchmark dataset for training and evaluating ranking functions for IR. In our experiments, the proposed method obtains a Mean Average Precision (MAP) score of 0.291, thereby significantly outperforming a non-linear baseline approach that uses Genetic Programming. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CEC.2013.6557983 | 2013 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) |
Keywords | Field | DocType |
engines,information retrieval system,probabilistic logic,search engines,genetic algorithms,web search engine,probability | Web search engine,Data mining,Search engine,Ranking SVM,Ranking,Computer science,Genetic programming,Ranking (information retrieval),Artificial intelligence,Probabilistic logic,Genetic algorithm,Machine learning | Conference |
Citations | PageRank | References |
2 | 0.36 | 24 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroyuki Sato | 1 | 2 | 1.03 |
| danushka bollegala | 2 | 692 | 66.77 |
| Yoshihiko Hasegawa | 3 | 28 | 4.05 |
| Hitoshi Iba | 4 | 1541 | 138.51 |