Browsing by Author "Khandelwal, Aseem"

Now showing 1 - 2 of 2
  • Thumbnail Image
    CarCool: The fun way of sharing rides
    Kansal, Samar; Srinivas, Shankar Srinidhi; Aggarwal, Ashish; Khandelwal, Aseem; Krishnaswamy, Krithika Gunasekaran (2024-05-03)
    CarCool redefines carpooling by connecting users with compatible companions through its innovative 'Vibe Score' algorithm. Users can update their interests in the profile section across categories like Lifestyle, Entertainment, Outdoor Activities, and Sports. These interests are converted into a normalized vector in the backend and stored in the database. When searching for rides, the 'Vibe Score' is calculated and displayed, allowing users to sort rides in descending order based on 'Vibe Score,' alongside other sorting options, ensuring personalized and enjoyable travel experiences.
  • Thumbnail Image
    Team 2 : Search and Recommendation
    Maheshwari, Ujjwal; Khandelwal, Aseem; Ram, Nikhil; Bhamidipati, Harsha; Banuelos, Jason (Virginia Tech, 2023-12-06)
    Theses and dissertations represent significant bodies of work accomplished by others, often containing remarkable contributions. The advent of electronic theses and dissertations (ETDs) aimed to simplify the storage and accessibility of these documents. However, their true value is realized when accompanied by an effective system for searching and retrieving specific documents. Our project involved building an Information Retrieval System that supports searching, ranking, browsing and recommendations for a large collection of ETDs. We divided the main goal into two modules - Search and Recommendation. Search is accomplished using Elasticsearch. An overview of the tool is given in the report, along with goals and the implementation process. A recommendation module will provide relevant recommendations for a user, built by experimenting with multiple algorithms in order to obtain the best results. The user manual has been provided for the reference of other groups. The developer manual includes how the project was developed, including architecture, data flow, module overviews, etc. The final report provides an overview of the tasks undertaken, how we planned to achieve our goals, milestones and our timelines. By the project's conclusion, we successfully scaled the system to manage 500K ETDs. Our efforts resulted in enhancements, particularly in bulk indexing and achieving faster response times for searches. Additionally, we refined the existing index schema and implemented a logging mechanism within Elasticsearch to accommodate logs from all collaborating teams.