Recommender systems. Increasingly recommender systems are being used to assist users with information discovery by bringing relevant content to users’ attention. They are part of a wider set of techniques for providing personalization: the tailoring of systems or services to the specific needs of individual users or communities [Smeaton and Xxxxxx, 2005; Adomavicius and Xxxxxxxx, 2005]. Recommendation mechanisms provide advice on objects depending on the user context or profile. They can be broadly classified by the strategy they employ (content-based or collaborative filtering) and by the recipient of the recommendations (individual user or group recommendations). Recommender functionality (and personalization more generally) has been proven useful when providing information access to cultural heritage [Xxxxxxxx, et al, 2012]. The PATHS project is investigating ways of assisting users with exploring a large collection of cultural heritage material taken from Europeana1, the European aggregator for museums, archives, libraries, and galleries [Agirre, et al, 2013a; Fernie, et al, 2012]. A prototype system has been developed that includes novel functionality for exploring the collection based on Google map-style interfaces, data-driven taxonomies and supporting the manual creation of guided tours or paths. Another aspect being explored is the use of recommendations to promote information discovery. To date we have been exploring non-personalized recommendations based on item-to-item co-occurrences. These provide recommendations of the kind “people who viewed this item also viewed this item.” Co-occurrence information (items that have been viewed consecutively in the same session) has been mined from a sample of Europeana logs to power the recommendations. Additionally, we provide links to “related items”, a form of content- based recommendation, based on identifying ‘similar’ items and classifying the type of relation. In this report we describe our recommendation work to date, difficulties in implementing recommendations and our experiments to check alternative techniques to improve recommendations. 1 Europeana website: xxxx://xxx.xxxxxxxxx.xx/portal/
Recommender systems. Recommender Systems (RS) support users in finding items of interest. The major goal of this subsection is to present the basic properties of the three major recommendation approaches: collaborative filtering (subsection 2.1.1), content-based filtering (subsection 2.1.2) and knowledge-based (subsection 2.1.3). Thereafter, we describe the properties of hybrid recommendation approaches which combine some of the basic variants listed above (subsection 2.1.4), and a summary of other techniques that are being used in recommendation systems (subsection 2.1.5). In addition to introducing the recommendation approaches, subsection 2.1.6 also includes a summary of how recommendation systems deal with context- awareness. Figure 1 summarizes this different approaches to recommender systems that will be explored in this subsection.
