Compared to the traditional recommendation algorithm based on item semantic with the precision of similarity calculated not high

we proposes an improved recommendation algorithm based on item semantics

through mining data sets

extracting content features

using probabilistic model and building feature vectors of items precisely

it calculates the similarity accurately between the items and combines with the results of recommendation algorithm based on semantic recommendation

not only its results is much better than the item-score recommendation algorithm for collaborative filtering and recommendation algorithm based on item semantics

but also it improves on the precision in the less users with scores.