Debugging Search Relevance

• A service that pictures how search engine "works" (with many simplifications obviously)
• You can think of this repo as a "mini" ElasticSearch (match & rank on inverted index)

Requirements

• Python 3.6 or above
• Install required libraries: pip install -r requirements.txt

How to Run

• Start the engine: cd into the root directory and run python3 app.py

• The engine is wrapped using flask so you can access it using POST request (e.g. using POSTMAN)

• Example /analyze request:

curl --location --request POST 'http://localhost:8080/analyze' \
  --header 'Content-Type: application/json' \
  --data-raw '{
    "keywords": "beli hape asus"
}'

• Example /analyze result: Note that the token beli is dropped and the token hape is normalized to hp See data/preprocessing/stopwords.txt and data/preprocessing/word_map.txt

{
  "data": {
      "tokens": [
          "hp",
          "asus"
      ]
  }
}

• Example /match request:

curl --location --request POST 'http://localhost:8080/match' \
  --header 'Content-Type: application/json' \
  --data-raw '{
      "keywords": "asus"
  }'

• Example /match result: Note that the token beli is dropped and the token hape is normalized to hp See data/preprocessing/stopwords.txt and data/preprocessing/word_map.txt

{
    "data": {
        "products": [
            {
                "desc": "cocok untuk gaming: DotA, CSGO, dkk",
                "match": {
                    "asus": {
                        "desc": [
                            "10"
                        ],
                        "title": [
                            "10",
                            "11"
                        ]
                    }
                },
                "title": "Laptop ASUS VivoBook 15"
            },
            {
                "desc": "hp asus murah banget dah asli murah, asus is the best, beli asus !",
                "match": {
                    "asus": {
                        "desc": [
                            "10"
                        ],
                        "title": [
                            "10",
                            "11"
                        ]
                    }
                },
                "title": "ASUS ROG Phone 5"
            }
        ]
    }
}

• Example /rank request:

curl --location --request POST 'http://localhost:8080/rank' \
  --header 'Content-Type: application/json' \
  --data-raw '{
      "keywords": "asus"
}'

• Example /rank result: Note that the token beli is dropped and the token hape is normalized to hp See data/preprocessing/stopwords.txt and data/preprocessing/word_map.txt

{
    "data": {
        "products": [
            {
                "ID": "11",
                "desc": "cocok untuk gaming: DotA, CSGO, dkk",
                "scores": {
                    "final_score": 0.1013662770270411,
                    "idf": {
                        "desc": {
                            "asus": 1.0986122886681098
                        },
                        "title": {
                            "asus": 0.4054651081081644
                        }
                    },
                    "tf": {
                        "desc": {},
                        "title": {
                            "asus": 0.25
                        }
                    }
                },
                "title": "Laptop ASUS VivoBook 15"
            },
            {
                "ID": "10",
                "desc": "hp asus murah banget dah asli murah, asus is the best, beli asus !",
                "scores": {
                    "final_score": 0.030371519345029976,
                    "idf": {
                        "desc": {
                            "asus": 1.0986122886681098
                        },
                        "title": {
                            "asus": 0.4054651081081644
                        }
                    },
                    "tf": {
                        "desc": {
                            "asus": 0.2727272727272727
                        },
                        "title": {
                            "asus": 0.25
                        }
                    }
                },
                "title": "ASUS ROG Phone 5"
            }
        ]
    }
}

How to Experiment

1. Adding/Deleting/Changing Products

• Alter products in data/products.json
• Reindex after altering: python3 index.py
• This will create a new data/inverted_index and data/term_count_index

2. Adding/Deleting/Changing Features

• Currently, this service only support string features
• Define the feature metadata in config.py, change the FEATURES variable
• Again, reindex after adding/deleting/changing features

3. Changing preprocessing

• Currently, I do the following: punctuation removal, lowercase, stopwords removal and normalization
• Please add/delete/change the preprocessing accordingly in preprocess function in preprocess.py

4. Changing matching algorithm

• This is a bit hard unfortunately, you need to understand and modify my code if you want to change the logic
• See function match in matching.py

5. Changing ranking algorithm

• If you just wanna switch the version of TF-IDF, please see config.py
• There I provide you with the raw and normalized version of TF-IDF
• However, if you wish to change further, that would be hard unfortunately
• You need to understand and modify my code/scoring function
• See function rank in ranking.py
• If you want to use something other than TF-IDF, replace compute_tf and compute_idf with your own function

Contact

• Should you have any questions, please shoot an email to geraldi.dzakwan@gmail.com
• If you are a Bukalapak's employee, please reach me on Slack (Geraldi Dzakwan)