Delivering A Personalised Experience For Library Users

The user experience is very important to NUS Libraries. With eight libraries under its wing, a good experience is not just about making it convenient for users to search across multiple databases; it is also about delivering personalised recommendations to engage them on wider and more in-depth use of library resources.

The National University of Singapore (NUS) is ranked consistently as one of the world’s top universities, and offers the most extensive college degree courses in the country. The university’s libraries, collectively known as NUS Libraries, offer a wide range of resources to support teaching and research in the various schools and faculties, their graduate divisions as well as administrative units and research institutes.

In December 2019, NUS Libraries launched a unified search system called FindMore that enables library users to perform searches across multiple internal and external databases and consolidates the results for them.

The next step now is to customise the user experience by augmenting the search engine and email newsletter with a recommender system. “Personalised experience for users is very important to us,” said university librarian Lee Cheng Ean. “This will encourage users to read more by uncovering more diverse yet relevant items from the university’s rich database of resources.”

To deliver this, NUS Libraries has embarked on its first attempt in developing an AI recommender. This is being done in partnership with AI Singapore (AISG), under the AISG 100 Experiments (100E) programme. The aim is to use AI to engage library users better by analysing their profiles and reading histories, in order to recommend items that may be of high relevance and interest to them.

In line with these requirements, the project team has developed 2 AI models:

  • Search-based recommender: to be integrated into FindMore, this model learns from the natural language features of a search term, the titles and descriptions of items in the database, and the historical transactions between every user and item. Based on these, the model is able to process a query in real-time and return a list of alternative recommended titles to complement the default search results, thereby increasing the diversity and novelty available to users.
  • Email recommender: to be integrated into subscribers’ email newsletters, this model learns from the individual’s profile and reading history, and merges this with the titles and descriptions of items in the database, and the historical transactions between every user and item. Based on these, the model is able to make title recommendations that are personalised to every subscriber.

The effectiveness of the model was evaluated via user testing of the AI recommender, where the solution achieved a click-to-open rate that was 4x higher than the global benchmark* for the education and training industry.

The team has also delivered a pipeline to automatically consume incremental data and update the model. This enables the system to make recommendations to NUS Libraries’ email subscribers on a weekly basis, suggesting relevant items based on their profiles, preferences and transactional history.

The AI recommender has been implemented in NUS Libraries and is accessible via the FindMore portal:

“We are excited to deploy the recommender on a larger scale as beta, and look forward to the system returning results which will encourage users to read a more diverse set of publications, We also hope to gather feedback that will enable us to further improve the system and continue to elevate the user experience.”

Cheng Ean


With the kind permission of NUS Libraries, the source code and sample (anonymised) dataset for the search-based recommender model has also been licensed under Apache 2.0 and made freely available for the public to access:


If you would like to know more about the 100E programme, please visit

You can also join the AISG Community and 100E Community groups here: