Inferring Networks of Substitutable and Complementary Products

In a modern recommender system, it is important to understand how products relate to each other. For example, while a user is looking for mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. These two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other. Here we develop a method to infer networks of substitutable and complementary products. We formulate this as a supervised link prediction task, where we learn the semantics of substitutes and complements from data associated with products. The primary source of data we use is the text of product reviews, though our method also makes use of features such as ratings, specifications, prices, and brands. Methodologically, we build topic models that are trained to automatically discover topics from text that are successful at predicting and explaining such relationships. Experimentally, we evaluate our system on the Amazon product catalog, a large dataset consisting of 9 million products, 237 million links, and 144 million reviews.Comment: 12 pages, 6 figure

OBOME - Ontology based opinion mining in UBIPOL

Ontologies have a special role in the UBIPOL system, they help to structure the policy related context, provide conceptualization for policy domain and use in the opinion mining process. In this work we presented a system called Ontology Based Opinion Mining Engine (OBOME) for analyzing a domain-specific opinion corpus by first assisting the user with the creation of a domain ontology from the corpus. We determined the polarity of opinion on the various domain aspects. In the former step, the policy domain aspect has are identified (namely which policy category is represented by the concept). This identification is supported by the policy modelling ontology, which describe the most important policy – related classes and structure. Then the most informative documents from the corpus are extracted and asked the user to create a set of aspects and related keywords using these documents. In the latter step, we used the corpus specific ontology to model the domain and extracted aspect-polarity associations using grammatical dependencies between words. Later, summarized results are shown to the user to analyze and store. Finally, in an offline process policy modeling ontology is updated

Review Mining: Hierarchy Generation for Online Reviews

In the present world of ecommerce more and more products are purchased and sold online then via any other medium. With such massive drive in online shopping more and more information is being added every day on web regarding the products and how good or bad are they. From the perspective of seller (such as Amazon) this information is very vital as this insight could be very helpful in making various decisions regarding inventory management, product pricing and so on. But the problem that arises in this context is the sheer volume of the reviews being added. In this paper we have proposed a way of extracting the semantics out of the reviews via use of various linguistic and statistical techniques. The idea is to extract the relevant information from the review and represent it in most concise format to make it more suitable for later processing

Using Project Management Techniques to Design a PMP Mathematics Study App for the Windows Universal Platform

Background As a late comer to the smartphone market, Microsoft has fallen behind the Apple and Google app ecosystems in the quantity and quality of apps offered. To attract developer talent, Microsoft released the Universal Windows Platform which enables apps to run across Windows devices with few additional modifications. Although the Windows app ecosystem has realized an increased number of available apps, few apps related to project management are currently available. About the project This project will design a PMP Certification Mathematics Study App for the Universal Windows Platform which will serve as a reference and study aid for the PMP certification exam. The app will be available to mobile and PC users who are utilizing the Microsoft Windows 10 and Windows 8 operating systems. Features of the app will include project management formula lookup, formula flashcards, and practice problems. At the completion of the project, the app will be submitted to the Windows Store for review and publishing to the Windows 10 application ecosystem. Approach The project scope will include the design of the app from requirements gathering to completion. Project deliverables will be aligned with Windows store applications evaluation criteria for responsiveness, reliability, and style. This project will conclude with submission of a completed application design to the project sponsor.Title Page / Table of Contents / List of Exhibits / Abstract / Background / About the project / Approach / Keywords / Introduction / Project Purpose / Project Approach / Research and Analysis / Research Approach / Research Analysis / Application Design Rating Verification / Research Objective 1: Investigate the preferred learning style of potential users / Research Objective 1: Design Conclusions and Implications / Flashcards Module / Formula Builder Module / Formula Reference Module / Research Objective 2: Investigate the most important aspect of user satisfaction / Research Objective 2: Design Conclusions and Implications / Research Conclusions / Requirements Gathering / User Interface Design / Project Deliverable Design / ViTech CORE / Input Application Requirements / Identify Application Components / Identify Component Functions / Identify Use Cases and Test Activities / Project Deliverables / Application Design Documents / Application Hierarchy / Conclusions and Recommendations / ViTech CORE Software Con/ lusions / Graphing Capabilities / Diagnostics Capabilities / Requirements Mapping and Verification / Final Project Deliverables / Recommendations for Further Research and Development / Application Publishing / Further Development and Product Updates / User Feedback Collection / Application Update Opportunities / Application Expansion Opportunitie