  There is an ongoing debate on personalization, adapting results to the unique
user exploiting a user's personal history, versus customization, adapting
results to a group profile sharing one or more characteristics with the user at
hand. Personal profiles are often sparse, due to cold start problems and the
fact that users typically search for new items or information, necessitating to
back-off to customization, but group profiles often suffer from accidental
features brought in by the unique individual contributing to the group. In this
paper we propose a generalized group profiling approach that teases apart the
exact contribution of the individual user level and the "abstract" group level
by extracting a latent model that captures all, and only, the essential
features of the whole group. Our main findings are the followings. First, we
propose an efficient way of group profiling which implicitly eliminates the
general and specific features from users' models in a group and takes out the
abstract model representing the whole group. Second, we employ the resulting
models in the task of contextual suggestion. We analyse different grouping
criteria and we find that group-based suggestions improve the customization.
Third, we see that the granularity of groups affects the quality of group
profiling. We observe that grouping approach should compromise between the
level of customization and groups' size.
