Modeling Product Search Relevance in e-Commerce
Table 2: Parameters used for the SVM
['[BOLD] Parameter', '[BOLD] Value', '[BOLD] Explanation']
For our baseline experiments we used two methods. First we used a unigram model i.e. extracted unigram features for the search term, product title and product description. Secondly, we computed OR and AND scores of the search term with the product title and the product description. These operators are explained in detail below. On both these methods of extracting feature sets, we used the Sequential Minimal Optimization (SMO) [platt1998sequential] Regression algorithm for training the SVM. The SMO is an effective way for solving the quadratic programming (QP) problem that arises during the training of support vector machines and is very commonly used in information retrieval systems. We chose to work with RBF (Radial Basis Function) kernel for the SVM. We have used 10-fold cross validation and generated relevance scores for the test data between 1.0 to 3.0. We chose to work with SVM because: We get to choose from multiple Kernels We can engineer the kernel (in our case find the optimal Gamma value) so that the model gains more knowledge about the features. SVM has a regularization parameter through which we can control over-fitting. SMO in particular is very successful in solving the convex optimization problem by which the SVM is defined. Authors in [van2004benchmarking] have thoroughly benchmarked the SVM classifiers.