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Institute
Embeddings for Product Data
(2022)
The E-commerce industry has grown exponentially in the last decade, with giants like Amazon, eBay, Aliexpress, and Walmart selling billions of products. Machine learning techniques can be used within the e-commerce domain to improve the overall customer journey on a platform and increase sales. Product data, in specific, can be used for various applications, such as product similarity, clustering, recommendation, and price estimation. For data from these products to be used for such applications, we have to perform feature engineering. The idea is to transform these products into feature vectors before training a machine learning model on them. In this thesis, we propose an approach to create representations for heterogeneous product data from Unite’s platform in the form of structured tabular records. These tables consist of attributes having different information ranging from product-ids to long descriptions. Our model combines popular deep learning approaches used in natural language processing to create numerical representations, which contain mostly non-zeros elements in an array or matrix called as dense representation for all products. To evaluate the quality of these feature vectors, we validate how well the similarities between products are captured by these dense representations. The evaluations are further divided into two categories. The first category directly compares the similarities between individual products. On the other hand, the second category uses these dense vectors in any of the above- mentioned applications as inputs. It then evaluates the quality of these dense representation vectors based on the accuracy or performance of the defined application. As result, we explain the impact of different steps within our model on the quality of these learned representations.
In the practice of software engineering, project managers often face the problem of software project management.
It is related to resource constrained project scheduling
problem. In software project scheduling, main resources are considered to be the employees with some skill set and required amount of salary. The main purpose of software
project scheduling is to assign tasks of a project to the available employees such that the total cost and duration of the project are minimized, while keeping in check that
the constraints of software project scheduling are fulfilled. Software project scheduling (SPSP) has complex combined optimization issues and its search space increases exponentially when number of tasks and employees are increased, this makes software project scheduling problem (SPSP) a NP-Hard problem. The goal of software project scheduling problem is to minimize total cost and duration of project which makes it multi-objective problem. Many algorithms are proposed up till now that claim to give near optimal results for NP-Hard problems, but only few are there that gives feasible set of solutions for software project scheduling problem, but still we want to get more efficient algorithm to get feasible and efficient results.
Nowadays, most of the problems are being solved by using nature inspired algorithms because these algorithms provide the behavior of exploration and exploitation. For solving
software project scheduling (SPSP) some of these nature inspired algorithms have been used e.g. genetic algorithms, Ant Colony Optimization algorithm (ACO), Firefly etc.
Nature inspired algorithms like particle swarm optimization, genetic algorithms and Ant Colony Optimization algorithm provides more promising result than naive and greedy algorithms. However there is always a quest and room for more improvement. The main purpose of this research is to use bat algorithm to get efficient results and solutions for software project scheduling problem. In this work modified bat algorithm is implemented where a different approach of random walk is used. The contributions of this thesis are to: (1) To adapt and apply modified multi-objective bat algorithm for solving software project scheduling (SPSP) efficiently, (2) to adapt and apply other nature inspired algorithms like genetic algorithms for solving software project scheduling (SPSP) and (3) to compare and analyze the results obtained by applied nature inspired algorithms and provide the conclusion.