1. A method of generating recommended items for a current user in an unfamiliar domain, comprising: selecting a plurality of candidate reference users sharing a user type with the current user in a reference domain, wherein the reference domain differs from the unfamiliar domain; calculating a plurality of user similarity indices for the plurality of candidate reference users, comprising calculating a user similarity index for a candidate reference user with respect to a reference domain and the current user, wherein the user similarity index is based at least in part on common behaviors between the current user and the candidate reference user in the reference domain, and is calculated according to history behavior data of a plurality of demarcated users in the unfamiliar domain and the reference domain, wherein the plurality of demarcated users are similar users in two or more domains that are different than the current user; selecting the candidate reference user as a reference user in the reference domain, responsive to the user similarity index of the candidate reference user satisfying a user similarity index condition; determining a domain similarity index between the unfamiliar domain and the reference domain, wherein the domain similarity index is distinct from the user similarity index, and wherein the domain similarity index indicates a degree of similarity between the unfamiliar domain and the reference domain; calculating a user weight for the reference user with respect to the current user, wherein the user weight is calculated by multiplying the user similarity index by the domain similarity index; generating two or more candidate items in the unfamiliar domain for the current user according to history behavior data of other users, comprising at least the reference user, in the unfamiliar domain; assigning an item weight to each of the two or more candidate items, wherein for each of the two or more candidate items, the item weight assigned is calculated by adding the user weights of the other users who are associated with the candidate item; and selecting, as the recommended items, a subset of the candidate items whose item weights meet an item weight condition.