Recommendation Technology Research Based On Machine Learning

With the de velopment of the Internet, the amount of data generated has been increasing every day, traditional search engi nes have been unable to f ully meet the current demand, the system has become the new star of the recomm ended age of the Internet, which is facing th e challenges of huge am ounts of data and give users m ore good experience and developed into a cros s-disciplinary. Recommendation system algorithm will often contain machine learning algorithms, and extensive application of cloud computing makes the machine learning algorithms can be ra pidly deployed. It also provides to enhance performance of the recommendation system.In this paper, the curren t machine learning algorithm and recommended system is studied. Using real data sets of Taobao and use Alibaba ODPS cl oud computing platform, and then based on hybrid technol ogy and deep learning technology to build two sets of recommendation system, changing something in Random Forest algorithm under conditions of unbalanced data for improvement.The main work is as follows:1.Using the Taobao data sets, Ali Mobile Rec, and ODPS platform for building recommendation system based on hybrid te chnology, the system uses a variety of mixed strategies, inclu ding hybrid tec hnology of features, m ulti-level hybrid recommendation technique, waterfall Hybr id technology, weighted recomm end mixing technology, Finally, a plurality of models integrate, achieved go od experimental results, F1 score is 8.11%, a nd on this basis, to verify the effect of improving the hybrid technology and the use of various conditions;2.Using Taobao Clothes Match datasets and ODPS platform building the recommendation system based on the deep learning, the system uses clothing packages of goods recommend by e xperts, try to extract the information contained in the pictures, where convolution neural network do the feature extraction, and proposed amendments to sort convolution neural network algorithm, experiments get a score about MAP 4.6%,whi ch shows that the algo rithm can im prove the effectiveness of the recommendation system;3.For the imbalance d ata, recommendation system often used, the imbalance algorithm existing in the Random Forests are further improved. Based on the waterfall hybrid technology for random sampling forest sample space make sample sampling spatial orientation, and im prove the origin al algorithm of the hierarchical spatial selection based on the adjustment of the classification adaptive strength. Experimental results show that the effect of these improvements can be improved to the algorithm.