Targeted Advertising Based on Browsing History
Table 6: Comparison of the percentage (%) accuracy of different websites representation methods
['Representation methods', 'Demo20 Gender', 'Demo20 Age', 'Demo100 Gender', 'Demo100 Age']
The last row is the representation method proposed in this paper. The ad variant of tf-idf weighting scheme is used. We can see that the new representation method achieves a much better performance than other approaches. The performance of category-based method relies on the completeness and precision of keyword library of each category, which is not easy to build. This may be the reason that category-based method only achieves average performance. LSI has not achieved very satisfactory performance either, demonstrating that topic extraction from messy website content is not an easy task. Comparing TF-IDF and our word embedding method, we can conclude that the incorporation of word vectors help significantly improve the representation power. TF-IDF representations are very sparse and with high dimension, resulting in difficulty in training a good classifier. What is of interest is that the sophisticated deep learning model RNN shows the worst performance. One reason is that website content usually contains a lot of words. RNN faces great challenges to memorize long sequences because of the problems of ‘vanishing gradient’ and ‘explosive gradient’. Another reason is that the content in websites is usually fragmented, therefore, this greatly limits the use of deep learning within our framework. Furthermore, sophisticated RNN model requires a great amount of time to train while our model is computationally efficient. The comparison results demonstrate the effectiveness and power of the new website representation method.