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Table 1.1 shows some statistics on recommendation coverage. Fiction titles are highly loaned and therefore collaborative filtering generates recommendations for 59% of the total fiction titles compared to only a 28% success percentage for the less loaned non-fiction titles. Collaborative filtering Combined with simple content-based filtering Fiction 59% 89% Non-Fiction 28% 53% Overall 34% 59%

Table 1.1 NLB’s recommendation coverage

The simple content-based filtering applied here uses only author and language fields. As can be seen, even combining collaborative filtering with a very straightforward content-based filtering algorithm significantly improves the recommendation coverage, raising the fiction success percentage from an acceptable 59% to an excellent 89% and non-fiction from a low 28% to an acceptable 53%. The recommendation coverage for patron level recommendations is even higher since the recommendations are the combination of the title level recommendations based on the patron’s recent loans.

2 CONNECTING UNSTRUCTURED DATA Unstructured data makes up a huge and growing portion of the content that NLB holds. Connecting these data will allow us to make these contents a lot more discoverable. One of our earliest attempts to connect such content involves using text analytics to perform “key phrase extraction”. The extracted key phrases are then used to perform searches which throw up other related content. These connections ensure that further discovery around the key topics are just 2 clicks away. The NLB “Infopedia” search-engine friendly micro site (http://infopedia.nl.sg) is a popular electronic encyclopedia on Singapore’s history, culture, people and events. With less than 2,000 articles, it attracts over 200,000 page views a month. It makes use of this approach alongside manual recommendations from the librarians. These manual recommendations by librarians are added as Librarian’s Recommendations and provides a 1 click access to related content.

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For more information on our setup, do feel free to contact us. To tackle the issue of OCR errors within the newspaper articles, we tuned the parameters for the text analytics algorithm to ignore infrequent word tokens. This has resulted in the removal of a significant portion of the OCR errors before the actual Mahout processing commenced. With the Apache Hadoop cluster set up and applying more aggressive algorithm parameters, we were able to generate similarity recommendations for more than 150,000 articles. The aggressive change in the parameter values are instrumental in slicing away a huge chunk of the time required for the processing. From being able to process 58,000 articles in around 18 hours, we are now able to process 150,000 articles in less than 30 minutes. However, we are still in the midst of resolving issues as we move towards our target of processing the 6 million articles. 3 NEXT STEPS We are currently working on additional proof-of-concepts with Apache Mahout particularly in the areas of on content enrichment, clustering and classification. In the area of content enrichment, there are lots of interesting ideas and possibilities for us to explore and conquer. We hope to enrich the content with semantic information so that content becomes connected semantically instead of just textually. We also hope to enrich the content with language translations to explore the possibilities of connecting content in different languages, given that we have four official languages in Singapore. On the clustering front, working with user-submitted personal memories from the Singapore Memory portal, Apache Mahout clustered the memories into 43 groups (Figure 8). The key terms amongst the memories within each cluster can then be analysed to identify the key theme for the clusters.

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software are now more matured and readily available through open source licenses. The time is ripe for libraries to leverage on them to bring out the enormous values in their collections. There are many more possibilities that we will explore in the future to make more and better connections – connections that allow users to discover comprehensive, relevant and trusted information. We do the work so that our users do not.