Can a Recommender System Induce Serendipitous Encounters?

This chapter presents a beginning effort to apply some ideas about serendipity to information retrieval and information filtering systems, especially in recommenders, to mitigate the over-specialization issue. The museum scenario is particularly interesting because items are arranged in a physical space and users interact with the environment. Thus disregarding context facets makes useless recommendations. Similar remarks are still valid in domains (different from cultural heritage fruition) in witch a physical or virtual space is involved and it represents a pragmatic justification to explain (supposed) serendipitous recommendations. As future work, we expect to carry out more extensive experimentation with more users and wider item collections. We plan also to gather user feedback and feeling by questionnaires focused on qualitative evaluation of the recommendations and the idea of getting suggestions that should surprise them. That is really important for the need to understand the effectiveness of the module in finding unknown items rather the ones that result best rated. Experimentation with users with different cultural levels and with different information seeking tasks are also important to find out which kind of user would like most serendipitous recommendations and to whom they are more useful. We expect also to implement the other suggestions given by Toms (Toms, 2000) and to develop further the heuristic proposed (maybe padding a parameter factor that multiplies the probabilities in order to balance better between categories) or also introduce new heuristics and make an experimental comparison.