  Social networking sites such as Flickr and Facebook allow users to share
content with family, friends, and interest groups. Also, tags can often assign
to resources. In the previous research using few association rules FAR, we have
seen that high-quality and efficient association-based tag recommendation is
possible, but the set-up that we considered was very generic and did not take
social information into account. The proposed method in the previous paper,
FAR, in particular, exhibited a favorable trade-off between recommendation
quality and runtime. Unfortunately, recommendation quality is unlikely to be
optimal because the algorithms are not aware of any social information that may
be available. Two proposed approaches take a more social view on tag
recommendation regarding the issue: social contact variants and social groups
of interest. The user data is varied and used as a source of associations. The
adoption of social contact variants has two approaches. The first social
variant is User-centered Knowledge, to contrast Collective Knowledge. It
improves tag recommendation by grouping historic tag data according to friend
relationships and interests. The second variant is dubbed 'social batched
personomy' and attempts to address both quality and scalability issues by
processing queries in batches instead of individually, such as done in a
conventional personomy approach. For the social group of interest, 'community
batched personomy' is proposed to provide better accuracy groups of
recommendation systems in contrast also to Collective Knowledge. By taking
social information into account can enhance the performance of recommendation
systems.
