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INF553-Homework 3 MinHash, LSH and Collaborative-Filtering Recommendation Systems Solved
In Assignment 3, you will complete two tasks. The goal is to let you be familiar with MinHash, Locality Sensitive Hashing (LSH), and different types of collaborative-filtering recommendation systems. The dataset you are going to play with is a subset from the Yelp dataset (https://www.yelp.com/dataset) used in the previous assignments.
1. Assignment Requirements
2.1 Programming Language and Library Requirements
a. You must use Python to implement all tasks. You can only use standard Python libraries (i.e., external libraries like Numpy or Pandas are not allowed). There will be a 10% bonus for each task (or case) if you also submit a Scala implementation and both your Python and Scala implementations are correct.
b. You are required to only use the Spark RDD to understand Spark operations. You will not receive any point if you use Spark DataFrame or DataSet.
2.2 Programming Environment
We will use Python 3.6, Scala 2.11, and Spark 2.3.2to test your code. There will be a 20% penalty if we cannot run your code due to the library version inconsistency.
2. Yelp Data
In this assignment, the datasets you are going to use are from: https://drive.google.com/open?id=11anZyiXBfhyICo2mkn6PFnegXgiDiUpF
We generated the following two datasets from the original Yelp review dataset with some filters such as the condition: “state” == “CA”. We randomly took 60% of the data as the training dataset, 20% of the data as the validation dataset, and 20% of the data as the testing dataset.
a. yelp_train.csv: the training data, which only include the columns: user_id, business_id, and stars.
b. yelp_val.csv: the validation data, which are in the same format as training data.
c. We do not share the testing dataset.
3. Tasks
3.1 Task1: Jaccard based LSH
In this task, you will implement the Locality Sensitive Hashing algorithm with Jaccard similarity using yelp_train.csv. You can refer to sections 3.3 – 3.5 of the Mining of Massive Datasets book.
In this task, we focus on the “0 or 1” ratings rather than the actual ratings/stars from the users. Specifically, if a user has rated a business, the user’s contribution in the characteristic matrix is 1. If the user hasn’t rated the business, the contribution is 0. You need to identify similar businesses whose similarity = 0.5.
You can define any collection of hash functions that you think would result in a consistent permutation of the row entries of the characteristic matrix. Some potential hash functions are:
f(x)= (ax + b) % m or f(x) = ((ax + b) % p) % m
where p is any prime number and m is the number of bins. You can use any combination for the parameters (a, b, p, and m) in your implementation.
After you have defined all the hashing functions, you will build the signature matrix. Then you will divide the matrix into b bands with r rows each, where b x r = n (n is the number of hash functions). You should carefully select a good combination of b and r in your implementation. Remember that two items are a candidate pair if their signatures are identical in at least one band.
Your final results will be the candidate pairs whose original Jaccard similarity is = 0.5. You need to write the final results into a CSV file according to the output format below.
Example of Jaccard Similarity:
user1 user2 user3 user4
business1
0
1
1
1
business2
0
1
0
0
Ja
ccard Similarity (business1, business2) = #intersection / #union = 1/3
Input format: (we will use the following command to execute your code)
Param: input_file_name: the name of the input file (e.g., yelp_train.csv), including the file path. Param: output_file_name: the name of the output CSV file, including the file path.
Output format:
IMPORTANT: Please strictly follow the output format since your code will be graded automatically. We will not regrade on formatting issues.
a. The output file is a CSV file, containing all business pairs you have found. The header is “business_id_1, business_id_2, similarity”. Each pair itself must be in the alphabetical order. The entire file also needs to be in the alphabetical order. There is no requirement for the number of decimals for the similarity value. Please refer to the format in Figure 2.
Figure 2: a CSV output example for task1
3.2 Task2: Recommendation System
In task 2, you are going to build different types of recommendation systems using the yelp_train.csv to predict for the ratings/stars for given user ids and business ids. You can make any improvement to your recommendation system in terms of the speed and accuracy. You can use the validation dataset (yelp_val.csv) to evaluate the accuracy of your recommendation systems.
There are two options to evaluate your recommendation systems. You can compare your results to the correspond ground truth and compute the absolute differences. You can divide the absolute differences into 5 levels and count the number for each level as following:
=0 and <1: 12345
=1 and <2: 123
=2 and <3: 1234
=3 and <4: 1234
=4: 12
This means that there are 12345 predictions with < 1 difference from the ground truth. This way you will be able to know the error distribution of your predictions and to improve the performance of your recommendation systems.
Additionally, you can compute the RMSE (Root Mean Squared Error) by using following formula:
Where Predi is the prediction for business i and Ratei is the true rating for business i. n is the total number of the business you are predicting.
In this task, you are required to implement:
Case 1: Model-based CF recommendation system with Spark MLlib
Case 2: User-based CF recommendation system
Case 3: Item-based CF recommendation system
Case 4: Item-based CF recommendation system with Jaccard based LSH
4.2.1. Model-based CF recommendation system
You will use Spark MLlib to implement this task. You can learn more about Spark MLlib by this link:
http://spark.apache.org/docs/latest/mllib-collaborative-filtering.html.
4.2.2. User-based CF recommendation system
4.2.3. Item-based CF recommendation system
4.2.4. Item-based CF recommendation system with Jaccard based LSH
For 4.2.4, you need to combine the MinHash and Jaccard based LSH algorithms in your item-based CF recommendation system. You will need a proper way to define similar business pairs. One example is like the “0-1 rating” in Task 1. Then you will incorporate these similar business pairs to help improve the recommendation system in terms of the speed and accuracy.
You should compare the results to the predictions from case 3. The recommendation system in this case should be either more efficient or more accurate than your system in case 3. You also need to explain how your LSH affects the recommendation system in this case. If you successfully improve your recommendation system and have a reasonable answer to the question, you will receive the points.
Input format: (we will use the following command to execute your code)
Param: train_file_name: the name of the training file (e.g., yelp_train.csv), including the file path
Param: test_file_name: the name of the testing file (e.g., yelp_val.csv), including the file path
Param: case_id: the case number (i.e., 1, 2, 3, or 4)
Param: output_file_name: the name of the prediction result file, including the file path
Output format:
a. The output file is a CSV file, containing all the prediction results for each user and business pair in the validation/testing data. The header is “user_id, business_id, prediction”. There is no requirement for the order in this task. There is no requirement for the number of decimals for the similarity values. Please refer to the format in Figure 3.
Figure 2: Output example in CSV for task2
b. A txt file includes your explanation for the case 4.2.4, named as “firstname_lastname_explanation”.
1. Assignment Requirements
2.1 Programming Language and Library Requirements
a. You must use Python to implement all tasks. You can only use standard Python libraries (i.e., external libraries like Numpy or Pandas are not allowed). There will be a 10% bonus for each task (or case) if you also submit a Scala implementation and both your Python and Scala implementations are correct.
b. You are required to only use the Spark RDD to understand Spark operations. You will not receive any point if you use Spark DataFrame or DataSet.
2.2 Programming Environment
We will use Python 3.6, Scala 2.11, and Spark 2.3.2to test your code. There will be a 20% penalty if we cannot run your code due to the library version inconsistency.
2. Yelp Data
In this assignment, the datasets you are going to use are from: https://drive.google.com/open?id=11anZyiXBfhyICo2mkn6PFnegXgiDiUpF
We generated the following two datasets from the original Yelp review dataset with some filters such as the condition: “state” == “CA”. We randomly took 60% of the data as the training dataset, 20% of the data as the validation dataset, and 20% of the data as the testing dataset.
a. yelp_train.csv: the training data, which only include the columns: user_id, business_id, and stars.
b. yelp_val.csv: the validation data, which are in the same format as training data.
c. We do not share the testing dataset.
3. Tasks
3.1 Task1: Jaccard based LSH
In this task, you will implement the Locality Sensitive Hashing algorithm with Jaccard similarity using yelp_train.csv. You can refer to sections 3.3 – 3.5 of the Mining of Massive Datasets book.
In this task, we focus on the “0 or 1” ratings rather than the actual ratings/stars from the users. Specifically, if a user has rated a business, the user’s contribution in the characteristic matrix is 1. If the user hasn’t rated the business, the contribution is 0. You need to identify similar businesses whose similarity = 0.5.
You can define any collection of hash functions that you think would result in a consistent permutation of the row entries of the characteristic matrix. Some potential hash functions are:
f(x)= (ax + b) % m or f(x) = ((ax + b) % p) % m
where p is any prime number and m is the number of bins. You can use any combination for the parameters (a, b, p, and m) in your implementation.
After you have defined all the hashing functions, you will build the signature matrix. Then you will divide the matrix into b bands with r rows each, where b x r = n (n is the number of hash functions). You should carefully select a good combination of b and r in your implementation. Remember that two items are a candidate pair if their signatures are identical in at least one band.
Your final results will be the candidate pairs whose original Jaccard similarity is = 0.5. You need to write the final results into a CSV file according to the output format below.
Example of Jaccard Similarity:
user1 user2 user3 user4
business1
0
1
1
1
business2
0
1
0
0
Ja
ccard Similarity (business1, business2) = #intersection / #union = 1/3
Input format: (we will use the following command to execute your code)
Param: input_file_name: the name of the input file (e.g., yelp_train.csv), including the file path. Param: output_file_name: the name of the output CSV file, including the file path.
Output format:
IMPORTANT: Please strictly follow the output format since your code will be graded automatically. We will not regrade on formatting issues.
a. The output file is a CSV file, containing all business pairs you have found. The header is “business_id_1, business_id_2, similarity”. Each pair itself must be in the alphabetical order. The entire file also needs to be in the alphabetical order. There is no requirement for the number of decimals for the similarity value. Please refer to the format in Figure 2.
Figure 2: a CSV output example for task1
3.2 Task2: Recommendation System
In task 2, you are going to build different types of recommendation systems using the yelp_train.csv to predict for the ratings/stars for given user ids and business ids. You can make any improvement to your recommendation system in terms of the speed and accuracy. You can use the validation dataset (yelp_val.csv) to evaluate the accuracy of your recommendation systems.
There are two options to evaluate your recommendation systems. You can compare your results to the correspond ground truth and compute the absolute differences. You can divide the absolute differences into 5 levels and count the number for each level as following:
=0 and <1: 12345
=1 and <2: 123
=2 and <3: 1234
=3 and <4: 1234
=4: 12
This means that there are 12345 predictions with < 1 difference from the ground truth. This way you will be able to know the error distribution of your predictions and to improve the performance of your recommendation systems.
Additionally, you can compute the RMSE (Root Mean Squared Error) by using following formula:
Where Predi is the prediction for business i and Ratei is the true rating for business i. n is the total number of the business you are predicting.
In this task, you are required to implement:
Case 1: Model-based CF recommendation system with Spark MLlib
Case 2: User-based CF recommendation system
Case 3: Item-based CF recommendation system
Case 4: Item-based CF recommendation system with Jaccard based LSH
4.2.1. Model-based CF recommendation system
You will use Spark MLlib to implement this task. You can learn more about Spark MLlib by this link:
http://spark.apache.org/docs/latest/mllib-collaborative-filtering.html.
4.2.2. User-based CF recommendation system
4.2.3. Item-based CF recommendation system
4.2.4. Item-based CF recommendation system with Jaccard based LSH
For 4.2.4, you need to combine the MinHash and Jaccard based LSH algorithms in your item-based CF recommendation system. You will need a proper way to define similar business pairs. One example is like the “0-1 rating” in Task 1. Then you will incorporate these similar business pairs to help improve the recommendation system in terms of the speed and accuracy.
You should compare the results to the predictions from case 3. The recommendation system in this case should be either more efficient or more accurate than your system in case 3. You also need to explain how your LSH affects the recommendation system in this case. If you successfully improve your recommendation system and have a reasonable answer to the question, you will receive the points.
Input format: (we will use the following command to execute your code)
Param: train_file_name: the name of the training file (e.g., yelp_train.csv), including the file path
Param: test_file_name: the name of the testing file (e.g., yelp_val.csv), including the file path
Param: case_id: the case number (i.e., 1, 2, 3, or 4)
Param: output_file_name: the name of the prediction result file, including the file path
Output format:
a. The output file is a CSV file, containing all the prediction results for each user and business pair in the validation/testing data. The header is “user_id, business_id, prediction”. There is no requirement for the order in this task. There is no requirement for the number of decimals for the similarity values. Please refer to the format in Figure 3.
Figure 2: Output example in CSV for task2
b. A txt file includes your explanation for the case 4.2.4, named as “firstname_lastname_explanation”.
1 file (538.8KB)
