INF553 - Foundations and Applications of Data Mining - Assignment 3  - Solved

1.   Overview of the Assignment
In Assignment 3, you will complete three tasks. The goal is to let you be familiar with Min-Hash, Locality Sensitive Hashing (LSH), and various types of recommendation systems.  

 

2.   Requirements
2.1 Programming Requirements 

a.       You must use Python to implement all the tasks. You can only use standard Python libraries (i.e., external libraries like numpy or pandas are not allowed).  

b.       You are required to only use Spark RDD, i.e. no point if using Spark DataFrame or DataSet.  

c.       There will be 10% bonus for Scala implementation in each task. You can get the bonus only when both Python and Scala implementations are correct.

 

2.2  Programming Environment 

Python 3.6, Scala 2.11, and Spark 2.3.0 
3.   Yelp Data  
In this assignment, we generated the review data from the original Yelp review dataset with some filters, such as the condition: “state” == “CA”. We randomly took 80% of the data for training, 10% of the data for testing, and 10% of the data as the blind dataset.  

You can access and download the following JSON files either under the directory on the Vocareum: resource/asnlib/publicdata/ or in the Google Drive (USC email only): https://drive.google.com/open?id=146Re0IDgtHB2OImmKOpzU43pGl12ZLqF a. train_review.json

b.       test_review.json – containing only the target user and business pairs for prediction tasks

c.       test_review_ratings.json – containing the ground truth rating for the testing pairs

d.       user_avg.json – containing the average stars for the users in the train dataset

e.       business_avg.json – containing the average stars for the businesses in the train dataset

f.        We do not share the blind dataset.

 

4.   Tasks
You need to submit the following files on Vocareum: (all lowercase)

a.       [REQUIRED] Python scripts: task1.py, task2train.py, task2predict.py, task3train.py, task3predict.py

b.       [REQUIRED] Model files: task2.model, task3item.model, task3user.model  

c.       [REQUIRED] Result files: task1.res, task2.predict  

d.       [REQUIRED FOR SCALA] Scala scripts: task1.scala, task2train.scala, task2predict.scala, task3train.scala, task3predict.scala; one Jar package: hw3.jar  

e.       [REQUIRED FOR SCALA] Model files: task2.scala.model, task3item.scala.model, task3user.scala.model

f.        [REQUIRED FOR SCALA] Result files: task1.scala.res, task2.scala.predict  

g.       [OPTIONAL] You can include other scripts to support your programs (e.g., callable functions).

 

4.1  Task1: Min-Hash + LSH (2pts)  
4.1.1 Task description

In this task, you will implement the Min-Hash and Locality Sensitive Hashing algorithms with Jaccard similarity to find similar business pairs in the train_review.json file. We focus on the 0 or 1 ratings rather than the actual ratings/stars in the reviews. 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.

Table 1 shows an example. Your task is to identify business pairs whose Jaccard similarity is >= 0.05. 

  

Table 1: The left table shows the original ratings; the right table shows the 0 or 1 ratings. 

 

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

𝑓(𝑥) = ((𝑎𝑥 + 𝑏) % 𝑝) % 𝑚

where 𝑝 is any prime number; 𝑚 is the number of bins. You can define any combination for the parameters (𝑎, 𝑏, 𝑝, or 𝑚) in your implementation.

After you have defined all the hash functions, you will build the signature matrix using Min-Hash. Then you will divide the matrix into 𝒃 bands with 𝒓 rows each, where 𝒃 × 𝒓 = 𝒏 (𝒏 is the number of hash functions). You need to set 𝒃 and 𝒓 properly to balance the number of candidates and the computational cost. Two businesses become a candidate pair if their signatures are identical in at least one band.

Lastly, you need to verify the candidate pairs using their original Jaccard similarity. Table 1 shows an example of calculating the Jaccard similarity between two businesses. Your final outputs will be the business pairs whose Jaccard similarity is >= 0.05.

                                                                      user1                  user2                   user3                  user4 

business1 
0 
1 
1 
1 
business2 
0 
1 
0 
0 
Table 2: Jaccard similarity (business1, business2) = #intersection / #union = 1/3 

 

4.1.2 Execution commands

Python
$ spark-submit task1.py <input_file> <output_file>
Scala
$ spark-submit --class task1 hw3.jar <input_file> <output_file>
 
<input_file>: the train review set

<output_file>: the similar business pairs and their similarities
 

4.1.3 Output format

You must write a business pair and its similarity in the JSON format using exactly the same tags as the example in Figure 1. Each line represents for a business pair (“b1”, “b2”). There is no need to have an output for (“b2”, “b1”).

  

Figure 1: An example output for Task 1 in the JSON format 

 


 

 

4.2  Task2: Content-based Recommendation System (2pts)
4.2.1 Task description

In this task, you will build a content-based recommendation system by generating profiles from review texts for users and businesses in the train review set. Then you will use the system/model to predict if a user prefers to review a given business, i.e., computing the cosine similarity between the user and item profile vectors.

During the training process, you will construct business and user profiles as the model:

a.       Concatenating all the review texts for the business as the document and parsing the document, such as removing the punctuations, numbers, and stopwords. Also, you can remove extremely rare words to reduce the vocabulary size, i.e., the count is less than 0.0001% of the total words.

b.       Measuring word importance using TF-IDF, i.e., term frequency * inverse doc frequency

c.       Using top 200 words with highest TF-IDF scores to describe the document

d.       Creating a Boolean vector with these significant words as the business profile

e.       Creating a Boolean vector for representing the user profile by aggregating the profiles of the items that the user has reviewed

During the predicting process, you will estimate if a user would prefer to review a business by computing the cosine distance between the profile vectors. The (user, business) pair will be considered as a valid pair if their cosine similarity is >= 0.01. You should only output these valid pairs.  

 



4.2.3 Output format:

Model format:  There is no strict format for the content-based model.  

Prediction format:

You must write the results in the JSON format using exactly the same tags as the example in Figure 2. Each line represents for a predicted pair of (“user_id”, “business_id”).  

  

Figure 2: An example prediction output for Task 2 in JSON format 

 


 

 

4.3  Task3: Collaborative Filtering Recommendation System (4pts)  
4.3.1 Task description

In this task, you will build collaborative filtering recommendation systems with train reviews and use the models to predict the ratings for a pair of user and business. You are required to implement 2 cases:

•       Case 1: Item-based CF recommendation system (2pts)

In Case 1, during the training process, you will build a model by computing the Pearson correlation for the business pairs that have at least three co-rated users. During the predicting process, you will use the model to predict the rating for a given pair of user and business. You must use at most N business neighbors that are most similar to the target business for prediction (you can try various N, e.g., 3 or 5). 

•       Case 2: User-based CF recommendation system with Min-Hash LSH (2pts)

In Case 2, during the training process, since the number of potential user pairs might be too large to compute, you should combine the Min-Hash and LSH algorithms in your user-based CF recommendation system. You need to (1) identify user pairs who are similar using their co-rated businesses without considering their rating scores (similar to Task 1). This process reduces the number of user pairs you need to compare for the final Pearson correlation score. (2) compute the Pearson correlation for the user pair candidates that have Jaccard similarity >= 0.01 and at least three co-rated businesses. The predicting process is similar to Case 1.

 

4.3.2 Execution commands Training commands:

Python
$ spark-submit task3train.py <train_file> <model _file> <cf_type>
Scala
$ spark-submit --class task3train hw3.jar < train_file> <model _file> <cf_type>  
 
<train_file>: the train review set

<model_file>: the output model

<cf_type>: either “item_based” or “user_based”
 

Predicting commands:

Python
$ spark-submit task3predict.py <train_file> <test_file> <model_file> <output_file> <cf_type>
Scala
$ spark-submit --class task3predict hw3.jar <train_file> <test_file> <model_file> <output_file> <cf_type>
 
<train_file>: the train review set

<test_file>: the test review set (only target pairs)

<model_file>: the model generated during the training process

<output_file>: the output results

<cf_type>: either “item_based” or “user_based”
 

4.3.3 Output format:

Model format:

You must write the model in the JSON format using exactly the same tags as the example in Figure 3. Each line represents for a business pair (“b1”, “b2”) for item-based model (Figure 3a) or a user pair (“u1”, “u2”) for user-based model (Figure 3b). There is no need to have (“b2”, “b1”) or (“u2”, “u1”).

  (a) 

  

(b) 

Figure 3: (a) is an example item-based model and (b) is an example user-based model 

 

Prediction format:

You must write a business pair and its similarity in the JSON format using exactly the same tags as the example in Figure 4. Each line represents for a predicted pair of (“user_id”, “business_id”).

  

Figure 4: An example output for task3 in JSON format