Optimizing E-Commerce Product Classification Using Transfer Learning

The global e-commerce market is snowballing at a rate of 23% per year. In 2017, retail e-commerce users were 1.66 billion and sales worldwide amounted to 2.3 trillion US dollars, and e-retail revenues are projected to grow to 4.88 trillion USD in 2021. With the immense popularity that e-commerce has gained over past few years comes the responsibility to deliver relevant results to provide rich user experience. In order to do this, it is essential that the products on the ecommerce website be organized correctly into their respective categories. Misclassification of products leads to irrelevant results for users which not just reflects badly on the website, it could also lead to lost customers. With ecommerce sites nowadays providing their portal as a platform for third party merchants to sell their products as well, maintaining a consistency in product categorization becomes difficult. Therefore, automating this process could be of great utilization. This task of automation done on the basis of text could lead to discrepancies since the website itself, its various merchants, and users, all could use different terminologies for a product and its category. Thus, using images becomes a plausible solution for this problem. Dealing with images can best be done using deep learning in the form of convolutional neural networks. This is a computationally expensive task, and in order to keep the accuracy of a traditional convolutional neural network while reducing the hours it takes for the model to train, this project aims at using a technique called transfer learning. Transfer learning refers to sharing the knowledge gained from one task for another where new model does not need to be trained from scratch in order to reduce the time it takes for training. This project aims at using various product images belonging to five categories from an ecommerce platform and developing an algorithm that can accurately classify products in their respective categories while taking as less time as possible. The goal is to first test the performance of transfer learning against traditional convolutional networks. Then the next step is to apply transfer learning to the downloaded dataset and assess its performance on the accuracy and time taken to classify test data that the model has never seen before

Efficient Optimization for Rank-based Loss Functions

The accuracy of information retrieval systems is often measured using complex loss functions such as the average precision (AP) or the normalized discounted cumulative gain (NDCG). Given a set of positive and negative samples, the parameters of a retrieval system can be estimated by minimizing these loss functions. However, the non-differentiability and non-decomposability of these loss functions does not allow for simple gradient based optimization algorithms. This issue is generally circumvented by either optimizing a structured hinge-loss upper bound to the loss function or by using asymptotic methods like the direct-loss minimization framework. Yet, the high computational complexity of loss-augmented inference, which is necessary for both the frameworks, prohibits its use in large training data sets. To alleviate this deficiency, we present a novel quicksort flavored algorithm for a large class of non-decomposable loss functions. We provide a complete characterization of the loss functions that are amenable to our algorithm, and show that it includes both AP and NDCG based loss functions. Furthermore, we prove that no comparison based algorithm can improve upon the computational complexity of our approach asymptotically. We demonstrate the effectiveness of our approach in the context of optimizing the structured hinge loss upper bound of AP and NDCG loss for learning models for a variety of vision tasks. We show that our approach provides significantly better results than simpler decomposable loss functions, while requiring a comparable training time.Comment: 15 pages, 2 figure