The Melvyl Recommender Project
<http://www.cdlib.org/inside/projects/melvyl_recommender/>, which
explored next-generation services for library catalogs, has reached its
conclusion. This project was funded by the Andrew W. Mellon Foundation.

Popular commercial services such as Google, eBay, 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. An additional extension project to the Melvyl
Recommender Project carried out deeper explorations into the most
interesting and promising questions raised during the original project,
and to add obvious missing pieces of functionality. The principal area
of investigation was the impact of adding full-text objects to what had
previously been a metadata-only index.

Overall findings from both portions 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, indicating that this is an approach worth pursuing for
providing ranking, recommendation and other types of functionality with
an online catalog.
* 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, although more investigation is required to
determine whether content-based ranking with or without different types
of weights (based on circulation or holdings) is more effective.
* 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.

Much of the functionality explored in both phases of the project can be
found in the Relvyl prototype:
    <http://rec-proto.cdlib.org/xtf/search?style=melrec&brand=melrec>

 More information about the entire project can be found on the CDL website:
    <http://www.cdlib.org/inside/projects/melvyl_recommender/>

--
Steve Toub
California Digital Library
(510) 987-0078