Chris Pollett > Students > Padmashali

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    [Bio]

    [Blog]

    [CS 297 Proposal]

    [Deliverable 1]

    [Netflix Recommender System - PDF File]

    [Deliverable 2]

    [Deliverable 3]

    [Deliverable 4]

    [CS 297 Report - PDF File]

    [CS 298 Proposal]

    [CS 298 Report - PDF File]

    [CS 298 Final Presentation Slides - PDF File]

























Project Blog

Summary of the Meeting on Mar 07, 2017
  1. Make a live recommendation table and an old table to not mess up the live table.

  2. Next time discuss how to incorporate natural words in the recommendation.

Summary of the Meeting on Feb 28, 2017
  1. Create a Media Job in Yioop for the recommendation system so that it runs periodically.

  2. Incorporate the timestamp feature so that it takes into account the changes since the last time recommendation system was run.

  3. Updates the recommendation table accordingly.

Summary of the Meeting on Feb 21, 2017
  1. Discuss integrating baseline predictor class.

  2. Build a new table Recommendation with columns user'_id, recommend_id, rating, recommend_type, etc.

  3. Make use of ITEM_SUMMARY table to build the recommendation system for Yioop

Summary of the Meeting on Feb 14, 2017
  1. Start Deliverable 1. Building standalone baseline predictor class

Summary of the Meeting on Feb 7, 2017
  1. CS 298 Project Proposal and discuss the deliverable

Summary of the Meeting on Jan 31, 2017
  1. Discussed the deliverables and CS 298 project proposal

Summary of the Meeting on Dec 13, 2016
  1. Review draft 2 - CS 297 Report

  2. Uploading all the deliverables and report

Summary of the Meeting on Dec 08, 2016
  1. Deliverable 4 code review

  2. Review draft 1 - CS 297 Report

Summary of the Meeting on Nov 29, 2016
  1. Discussed the implementation for deliverable 4 - Latent matrix factorisation using gradient descent

  2. Discussed how to make the final report for CS 297

Summary of the Meeting on Nov 22, 2016
  1. Presentation - HITS algorithm

  2. Finished Deliverable 3 - Patch 2

Summary of the Meeting on Nov 15, 2016
  1. Presentation - SVD for recommendations

  2. Finished Deliverable 3 - Patch 1

  3. Start Deliverable 3 - Patch 2

Summary of the Meeting on Nov 08, 2016
  1. Presentation - Latent Matrix Factorization using gradient descent

  2. Deliverable 3 - Yioop patch 1 progress update

Summary of the Meeting on Nov 02, 2016
  1. Presentation - Practical Machine Learning Book by Ted Dunning and Ellen Friedman

  2. Discuss the progress on Yioop Manage account Page

Summary of the Meeting on Oct 25, 2016
  1. Reviewed the code.

  2. Validate email address at the client side along with server side.

  3. Password strength checks and form should not submit if all fields are not filled.

  4. In manage account page add a join date field and filter based on join dates.

Summary of the Meeting on Oct 18, 2016
  1. Reviewed the code and discussed about optimizing the code so that the computer doesn't crash while processing and analyzing 2.7 million yelp review records.

  2. Make changes to the code and upload Deliverable 2

  3. Present Practical Machine Learning Book by Ted Dunning and Ellen Friedman.

  4. Find a list of stuff about Yioop user interface which I do not like.

  5. Understand the Yioop Framework.

Summary of the Meeting on Oct 11, 2016
  1. Discussed about baseline predictors implementation and predicting users rating for Yelp business/restaurants based on reviews.

  2. Completed presenting Chapter 2 from who's #1 ? The science of rating and ranking

Summary of the Meeting on Oct 04, 2016
  1. Present chapter 1 and 2 from who's #1 ? The science of rating and ranking

  2. Deliverable 1 completion

  3. Start Collaborative filtering Implementation

Summary of the Meeting on Sep 27, 2016
  1. Presented the Max Flow Algorithms

  2. Demonstrated the max flow implementation - Ford Fulkerson and push relabel algorithms

  3. For next week present the Netflix recommender system

  4. For next week present chapter 1 and 2 from who's #1 ? The science of rating and ranking

  5. Finish the push relabel code and add simple test cases for the max flow implementation. Write a summary for the code uploaded.

Summary of the Meeting on Sep 20, 2016
  1. Presented the Max Flow Algorithms

  2. Discussed Networked Life Chapter 4

  3. For next week demonstrate the Max Flow Algorithm - Deliverable 1 due

Summary of the Meeting on Sep 13, 2016
  1. Prepare Presentation for Max Flow propblem. Implement Max Flow problem in PHP. Present the NETFLIX recommendation Engine algorithm - chapter 4 Networked Life.

  2. Update Proposal

  3. Finalize the two algorithms for recommendation engine which needs to be implemented.

Summary of the Meeting on Sep 6, 2016 Discussed the Deliverables as below:
  1. Deliverable 1: Max Flow Presentation and Implementation.
  2. Deliverable 2: Recommendation Engine Algorithms - Literature Review
  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 Implementation.
  5. Deliverable 5: CS297 Final Report
  6. For next week: Read chapter 26 from Algorithms book