| Popis: |
Existing classification techniques over e-commerce data are mainly based on the users' purchasing patterns. However, gender preferences significantly improve in recommending various products, targeting customers for branding products, providing customized suggestions to the users, etc. We explain three methods for gender-based classification. All the methods are two-phased in which the features are extracted in the first phase. Classification of gender is done in the second phase based on the features identified in the first phase. The first technique exploits the hierarchical relationships among products and purchasing patterns. In the first phase, dimensionality is reduced from data by identifying the features that well describe the browsing pattern of the users. The second phase uses these features to classify gender. The second technique extracts both basic and advanced features. It uses the random forest to classify the data based on features identified. The third approach extracts behavioral and temporal features along with product features, and classification is done using gradient-boosted trees. Experiments were also conducted on the state-of-the-art classification algorithms. |
