Popular commercial services such as Google, e-Bay, Amazon, and Netflix have evolved quickly over the last decade to help people find what they want, developing information retrieval strategies such as usefully ranked results, spelling correction, and recommendations. Library catalogs, in contrast, have changed little and are not well equipped to meet changing needs and expectations. The Melvyl Recommender Project explored methods and feasibility of closing this gap.
Overall findings of the project include:
- The text-based discovery application, the eXtensible Text Framework (XTF) that was the backbone of the project’s system (known as “Relvyl”) proved capable of scaling to millions of records and hundreds of concurrent users
- Use of an index based single word spelling correction algorithm addressed 90 percent of misspelled single words
- Initial examination of faceted browsing and FRBR-like document groups indicated that each of these features could substantially improve the patron’s experience of working with large result sets
- User assessment confirmed that users prefer relevance ranked results over unranked results
- Two types of recommendation strategies were explored: circulation-based (“patrons who checked this out also checked out…”) and text-similarity (“More like this…”). User assessment was conducted against the first type and showed that users like getting recommendations, which are useful for performing academic tasks, and they can also serve a unique query expansion function
- Adjustments to keyword searching strategies, document scoring and the index-based spelling correction dictionary allowed for an effective combination of full-text and metadata only records into one system, in which neither type of record was privileged