Library Recommendation System with Diverse ML Techniques - 12/03/2024 15:41 EDT

  • Tila: Closed
  • Palkinto: $20
  • Vastaanotetut työt: 7
  • Voittaja: hrshammo

Kilpailun tehtävänanto

I want a proficient developer to create a recommendation system geared towards library users. It should employ a variety of machine learning techniques including KNN, MF, Association Rule Mining, Decision Tree, Naive Bayes, MLP, and Ensemble Learning. The system’s effectiveness should be evaluated using several methods as below:

- RMSE
- MAE
- MSE
- Accuracy
- Precision Recall
- F1 score
Use Book Crossing Dataset
Code must be in python and don't use specific recommender system library i.e., surprise etc.

The successful freelancer must have appreciable knowledge and expertise across these various machine learning techniques, and previous experience building effective recommendation systems. Experience working on recommendation systems for a library or similar institution would be a considerable plus. Ensuring precise evaluations using the mentioned techniques is paramount to the project's success. Remember, the end users of this system will be library users, so the system must be optimal in delivering relevant recommendations.
Note: upload complete code of data preparation, recommender engine and evaluation in python. Write python code in all given ml technique separately and also make recommendation engine using ensemble learning. Best code will be awarded.

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Julkinen selvennystaulu

  • zferd
    zferd
    • 2 kuukautta sitten

    Sorry but I don't get it. What is recommendation system geared towards library users? Can you give an example scenario?
    Is that the user will input its data, and we will generate a machine learning models that suits the given data or goal based on some metrics such as rmse, etc. Then recommend to the user what is the best algorithm to use for his/her data.

    • 2 kuukautta sitten
    1. Harry078
      Kilpailun järjestäjä
      • 2 kuukautta sitten

      You can recommend book to library users. In this project, you can use book crossing dataset and develop collaborative fitering based recommender system using given ml techniques. Use only user id, itemId and ratings.

      • 2 kuukautta sitten
    2. zferd
      zferd
      • 2 kuukautta sitten

      Thanks for the info. I am not participating the contest, time is too short. Good luck with the contest.

      • 2 kuukautta sitten
  • ikhan9985
    ikhan9985
    • 2 kuukautta sitten

    #increaseprize

    • 2 kuukautta sitten

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