The dataset is in .json format here. After downloading Software reviews and metadata, we ran this script to get data ready for preprocessing.

1. Licensing Fee is set to 80% of the minimum price in the software category. Llicensing fees could be similar in a particular software category and country.

2. Implementation Cost is set to 50% of the cost of the software.

3. Maintenance cost is assumed to be a monthly service so it was set to 1% of the price of the product.

1. pip install --upgrade pip
2. python -m venv .llmrs
3. source .llmrs/bin/activate
4. conda deactivate
5. pip install -r requirements.txt

1. run python src/recommendation_api.py

2. visit 127.0.0.1:500

   a. Enter Software description with price, license, maintenace and implementation costs in the respective boxes.

   b. When you click Get Recommendation, this would load pre-processed data/softwares_with_scores.csv and compute similarity with input software specification from user input.

   c. Output is then ranked with our ranking algorithm and parsed to the web interface

"Software for managing employee files"

price = 0,10

license cost = 0, 10

maintenance cost = 0, 10

implementation cost= 0, 10

The pipeline contains 3 steps as follows:

1. Data preprocessing
2. Process Software Data
3. Recommender