  This paper focuses on the recommendation of events in the Social Web, and
addresses the problem of finding if, and to which extent, certain features,
which are peculiar to events, are relevant in predicting the users' interests
and should thereby be taken into account in recommendation.
  We consider in particular three "additional" features that are usually shown
to users within social networking environments: reachability from the user
location, the reputation of the event in the community, and the participation
of the user's friends. Our study is aimed at evaluating whether adding this
information to the description of the event type and topic, and including in
the user profile the information on the relevance of these factors, can improve
our capability to predict the user's interest.
  We approached the problem by carrying out two surveys with users, who were
asked to express %with a score their interest in a number of events. We then
trained, by means of linear regression, a scoring function defined as a linear
combination of the different factors, whose goal was to predict the user
scores. We repeated this experiment under different hypotheses on the
additional factors, in order to assess their relevance by comparing the
predictive capabilities of the resulting functions.
  The compared results of our experiments show that additional factors, if
properly weighted, can improve the prediction accuracy with an error reduction
of 4.1%. The best results were obtained by combining content-based factors and
additional factors in a proportion of approximately 10:4.
