The bandwagon attack uses selected items which are likely to have been rated
by a large number of users in the database. These items are assigned the maximum
rating value together with the target item it . The ratings for the filler items are determined randomly in a similar manner as in the random attack. The bandwagon attack
therefore can be viewed as an extension of the random attack.
As we show in Section 25.4, the bandwagon attack is nearly as effective as the
average attack against user-based collaborative filtering algorithms2, but without the
knowledge requirements of that attack. Thus it is more practical to mount. However,
as in the case of the average attack, it falls short when used against an item-based
algorithm [12].