Learning to rank for educational search engines
| Autor: | Ismail Sengor Altingovde, Özgür Ulusoy, Rifat Ozcan, Arif Usta |
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| Přispěvatelé: | Usta, Arif, Ulusoy, Özgür |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Context model
Information retrieval Computer science 4. Education 05 social sciences Feature extraction InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Educational search Query-dependent ranking General Engineering 02 engineering and technology Computer Science Applications Education Ranking (information retrieval) Set (abstract data type) Search engine 020204 information systems Learning to rank (LTR) Search engines 0202 electrical engineering electronic engineering information engineering Task analysis Domain knowledge Learning to rank 0509 other social sciences 050904 information & library sciences |
| Zdroj: | IEEE Transactions on Learning Technologies |
| Popis: | In this digital age, there is an abundance of online educational materials in public and proprietary platforms. To allow effective retrieval of educational resources, it is a necessity to build keyword-based search engines over these collections. In modern Web search engines, high-quality rankings are obtained by applying machine learning techniques, known as learning to rank (LTR). In this article, our focus is on constructing machine-learned ranking models to be employed in a search engine in the education domain. Our contributions are threefold. First, we identify and analyze a rich set of features (including click-based and domain-specific ones) to be employed in educational search. LTR models trained on these features outperform various baselines based on ad-hoc retrieval functions and two neural models. As our second contribution, we utilize domain knowledge to build query-dependent ranking models specialized for certain courses or education levels. Our experiments reveal that query-dependent models outperform both the general ranking model and other baselines. Finally, given well-known importance of user clicks in LTR, our third contribution is for handling singleton queries without any click information. To this end, we propose a new strategy to “propagate” click information from the other, similar, queries to the singleton queries. The proposed click propagation approach yields a better ranking performance than the general ranking model and another baseline from the literature. Overall, these findings reveal that both the general and query-dependent ranking models, trained using LTR approaches, yield high effectiveness in educational search, which may ultimately lead to a better learning experience. |
| Databáze: | OpenAIRE |
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