CS297 Proposal

Project Topic - Recommendation Engine for Yioop Groups

Sarika Padmashali (sarika.padmashali@sjsu.edu)

Advisor: Dr. Chris Pollett

Description:

Yioop is an open source search engine, wiki system and user discussion group system. A recommendation system analyzes user behaviour on a website to make suggestions about what a user should do in the future on the website. For this project we will develop a recommendation system for discussion groups for Yioop.

Schedule:

Week 1: August 30,2016 - September 05,2016	Finalise the topic for CS297 and start preparing for project proposal
Week 2: Seotember 06,2016 - September 12,2016	Prepare the Project proposal. Read papers on recommendation engine. Discuss the deliverables and discuss the project in detail.
Week 3: September 13,2016 - September 19,2016	Submit Project Proposal. Update the blog and bio. Read and Implement Max Flow problem efficiently in php. Read and present the chapter from networked life about NETFLIX recommendation system.
Week 4: September 20,2016 - September 26,2016	Deliverable 1: Presentation and implementation of Max Flow Problem. Implement the ford fulkerson and push relabel algorithm.
Week 5: September 27,2016 - October 03,2016	Read chapters from who's #1? The science of ranking and rating. Papers on recommendation engines.
Week 6: October 04,2016 - October 10,2016	Start implementing collaborative filtering.
Week 7: October 11,2016 - October 17,2016	Deliverable 2: Recommendation Engine Algorithm Implementation - Collaborative Filtering (Baseline Predictors).
Week 8: October 18,2016 - October 24,2016	Download Yioop and understand the framework.
Week 9: October 25,2016 - October 31,2016	Improve Yioop Manage account Page
Week 10: November 1,2016 - November 07,2016	Deliverable 3: Suggest and implement changes for the Yioop Manage Group Activity and manage account page.
Week 11: November 08,2016 - November 14,2016	Read papers on Recommendation engine
Week 12: November 15,2016 - November 21,2016	Understand Latent matrix factorization method for recommendation.
Week 13: November 22,2016 - November 28,2016	Deliverable 4: Recommendation Engine Algorithm Implementation - Latent matrix factorization.
Week 14: November 29,2016 - December 06,2016	Start Preparing the CS 297 Report
Week 15: December 07,2016 - December 12,2016	Review the Report
Week 16: December 13,2016 - December 19,2016	Deliverable 5: CS 297 Final Project Report

Deliverables:

The full project will be done when CS298 is completed.

The following will be done by the end of CS297:

1. Deliverable 1: Max Flow Presentation and Implementation in Python - Ford Fulkerson Algorithm and Push Relabel Algorithm.

2. Deliverable 2: Recommendation Engine Algorithm Implementation - Baseline predictors (Collaborative Filtering)

3. Deliverable 3: Download Yioop. Suggest something on manage account page. Try to improve some aspect of manage group activity.

4. Deliverable 4: Recommendation Engine Algorithm - Latent Matrix Factorization Implementation (Collaborative filtering)

5. Deliverable 5: CS297 Final Report.

References:

Cormen, T., Leiserson, C. and Rivest, R. (1990). Introduction to algorithms. Cambridge, Mass.: MIT Press.