Abstract:  Collaborative filtering-based recommender systems，which automatically predict preferred products of a
user using known preferences of other users, have become extremely popular in recent years due to the increase in
web-based activities such as c-commerce and online content distribution. However, traditional collaborative filtering
techniques provide poor accuracy,a large number of ratings from similar users or similar items arc not available,duc
to the sparsity inherent to rating data. Consequently, prediction quality can be poor.To address the matter,a new col-
laborative filtering recommendation algorithm based on item category and interest measure is proposed. In this algo-
rithm, first，the item categories similarity matrix is constructed by calculating the iterrritem category distance，and
then analyzes the correlation degree of different items by using Piatetsky-Shapiro interestingness mcasure,at last，a
novel collaborative filtering algorithm is proposed after combining the information of item categories with iterrritem
interestingness and utilizing ameliorated conditional probability method as the standard iterrritem similarity measure.
Empirical evaluation of the algorithm on large movie rating datasets demonstrates that it is not only an effective solu-
tion to data sparisity and the drawbacks of traditional similarity method，but also improves the accuracy of user inter-
est and nearest neighbor search. At the same time, this algorithm achieves better prediction accuracy compared to
other well-performing collaborative filtering algorithms.