Abstract: With the era of big data’s coming,the amount of application data increases sharply,the result of which gives more and more prominence to a personalized recommendation technique.However,traditional recommendation techniques applied to big data are confronted with some problems,such as low recommendation accuracy,long recommendation time and high network traffic.Therefore,the performance of recommendation degrades drastically.To address this issue,a user co-occurrence matrix recommendation strategy was proposed in this paper. The user for the project’s predi-cation rating matrix is got by multiplying user similarity matrix and item rating matrix.Candidate items set is generated for each user using user similarity matrix multiply by item similarity matrix.On this basis,traditional collaborative filtering algorithms were parallel expand according to the feature of distributed processing architecture,and a distributed collaborative filtering algorithm was designed.Finally,multi-sub tasks are in series utilizing combination of redefined MapReduce schema to execute automatically.Experimental results show that our approach achieve better prediction accuracy and efficiency in distribute computing environment.

Key words: Collaborative filtering,Recommendation systems,Distributed computing,Big data