The invention discloses a recommendation method based on standard labels and item grades. The recommendation method is characterized in that the labels are standardized, namely, user-defined labels are mapped to the standard labels clear and definite in
semantics, then the standard labels are used for establishing user interest models, according to the user interest models, the similarity among users is calculated, neighboring user groups are established, and then the grades of the items to be graded by the users are predicted based on item grades of the target user and the neighboring users of the target user and an improved Slope one
algorithm, so that personalized recommendation is achieved. Availability of the labels which are widely used on the Web2.0 internet and can be subjected to free defining can be obviously improved, the similarity among the users is calculated by the utilization of the user interest models based on the standard labels, the similar user groups are established for the target user, the search range of the related item grades of the target user can be shrunk, the calculation amount of the
algorithm can be reduced, item grade prediction through the Slope one
algorithm is improved, contributions to grade prediction are improved for the users similar in hobbies and interests, and therefore personalized
recommendation quality of
the internet is improved.