With 8+ years of diverse experience in product management and software engineering, I'm a seasoned Product Manager and a former Software Engineer. From SaaS to IoT, Job Search to Fintech, Cloud to CAD, Online Compliance to CMS, I've immersed myself in various domains.
JIRA- Classic and NextGen, Product Roadmap, Confluence, Asana, Slack, Teams, Product Board, QMetry, Google Analytics, Admanager, BigQuery, AnnounceKit, Miro, Amplitude
English, Italian
Swift, Java, Kotlin, Objective-C, JavaScript, Python, R, SQL
XCode, Android Studio, VSCode, Orange, Jupyter, Anaconda, Eclipse, IntelliJ IDEA, BigQuery, Marvel, SourceTree
Docker, Openvidu, AWS Lamba, EC2, AWS AppSync, Dynamo DB, Mongo DB, Redis, AWS Cognito, Github, Bitbucket, Gitlab, SQL
A-CSPO (Product Owner)
CSPO (Product Management)
IELTS 8.0
JIRA Service Management, Tableau, Marvel, XML, JSON
Send your queries to
Italy
The use of Collaborative Filtering is becoming very popular in designing a simple yet efficient recommender system. A recommender system based on Collaborative Filtering basically predicts a user's interest in some item on the basis of the scores generated & the correlation calculated between the users. In this paper we propose a basic structure & steps of designing a recommender system that uses Collaborative Filtering (user based) along with applications of partitioning &and clustering of data, thus designing a Recommender System. The proposed system reduces the complexity & gives a clear view of the approach to build a recommender system from scratch.
Due to steady growth of e-commerce market, advancement in data mining and data intelligence technologies, recommender systems are becoming more powerful and accurate. Not only it has the prospects to help the customers as discussed in the previous paper, Collaborative Filtering Based Simple Restaurant Recommender, the businesses can also drive the sales of their products and generate more revenue. This paper is an extension to Collaborative Filtering Based Simple Restaurant Recommender aimed at giving procedures and steps to build a generic product recommender based on user based collaborative filtering, by using open source platform, Apache mahout.
Umar Farooque ©
2024