Monthly Archives: July 2017

InstaCart: Product Recommender

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InstaCart has recently open sourced anonymized data on customer orders for its Kaggle competition. You can find out more info from the link below.

https://www.kaggle.com/c/instacart-market-basket-analysis

Previously, I did some data exploration to discover some interesting insights. See my previous blog post.

Next I would like to use their anonymized data to build a product recommender system. There are many approaches/strategies in product recommendations, eg. Most popular items, Also bought/viewed, Featured items, and etc. I am going to explore the most popular items approach and collaborative filtering (also bought) approach.

We can identify the top 5 popular items for each department and utilize them for most popular items recommendation. For example, the top 5 items for frozen department are Blueberries, Organic Broccoli Florets, Organic Whole Strawberries, Pipeapple Chunks, Frozen Organic Wild Blueberries.

Screen Shot 2017-07-10 at 10.51.55 PM

Next, I utilized Spark ALS algorithm to  build a collaborative filtering based recommender system.

Some stats about the data

Total user-product pair (rating): 1,384,617

Total users: 131,209

Total products: 39,123

Given the data, I splitted the data into train set (0.8) vs test set (0.2) randomly. This resulted in
Number of train elements: 1,107,353
Number of test elements: 277,264

Here are the parameters I used in ALS, rank = 10, lambda = 1, number of iterations = 10, 50, 60, 80

  • Number of iterations = 10,    RMSE = 0.9999895
  • Number of iterations = 50,    RMSE = 0.9999828
  • Number of iterations = 60,    RMSE = 0.9999875
  • Number of iterations = 80,    RMSE = 0.9999933

We can also try out different ranks in grid search.

Here are some recommendations suggested by ALS collaborative filtering algorithm (number of iterations =80, rank=10)

Given user 124383 Transaction History

+——-+———-+—–+———-+—————————+——–+————-+
|user_id|product_id|count|product_id|product_name |aisle_id|department_id|
+——-+———-+—–+———-+—————————+——–+————-+
|124383 |49478 |1 |49478 |Frozen Organic Strawberries|24 |4 |
|124383 |21903 |1 |21903 |Organic Baby Spinach |123 |4 |
|124383 |19508 |1 |19508 |Corn Tortillas |128 |3 |
|124383 |20114 |1 |20114 |Jalapeno Peppers |83 |4 |
|124383 |44142 |1 |44142 |Red Onion |83 |4 |
|124383 |20345 |1 |20345 |Thin Crust Pepperoni Pizza |79 |1 |
|124383 |27966 |1 |27966 |Organic Raspberries |123 |4 |
+——-+———-+—–+———-+—————————+——–+——————+

Here are the recommendations
+———-+————————————————————-+——–+————-+
|product_id|product_name |aisle_id|department_id|
+———-+————————————————————-+——–+——————+
|28717 |Sport Deluxe Adjustable Black Ankle Stabilizer |133 |11 |
|15372 |Meditating Cedarwood Mineral Bath |25 |11 |
|18962 |Arroz Calasparra Paella Rice |63 |9 |
|2528 |Cluckin’ Good Stew |40 |8 |
|21156 |Dreamy Cold Brew Concentrate |90 |7 |
|12841 |King Crab Legs |39 |12 |
|24862 |Old Indian Wild Cherry Bark Syrup |47 |11 |
|37535 |Voluminous Extra-Volume Collagen Mascara – Blackest Black 680|132 |11 |
|30847 |Wild Oregano Oil |47 |11 |
+———-+————————————————————-+——–+————-+

 

 

 

 

 

Instacart Data Insight

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InstaCart has recently organized a Kaggle competition and published anonymized data on customer orders over time to predict which previously purchased products will be in a user’s next order. You can check out the link below.

https://www.kaggle.com/c/instacart-market-basket-analysis

First, I used Spark to analyze the data to uncover hidden insights.

We were given the departments data

Screen Shot 2017-07-05 at 10.04.34 PM

Products data

Screen Shot 2017-07-05 at 10.07.32 PM

Orders data

Screen Shot 2017-07-05 at 10.07.47 PM

Lets find out which is the busiest day of the week. As it turns out, day 0 is the busiest day, with 600,905 orders followed by day 1 with 587478 orders. I would assume day 0 is Sunday and day 1 is Monday.

Screen Shot 2017-07-05 at 10.15.12 PM.png

Next, lets figure out which is the busiest hour in a day. It turns out 10 am is the busiest hour with 288,418 orders, followed by 11 am with 284,728 orders.

As you can see, Instacart customers like to shop from 11 am to 5 pm. It would be interesting to see the day of week + hour of day breakdown too.

Screen Shot 2017-07-05 at 10.18.24 PM

Here is the breakdown of popular hours for each day. 10 am dominates the top spot.

Screen Shot 2017-07-06 at 12.59.15 AM

Next lets figure out the top ten popular items among Instacart customers. Surprisingly, banana is the most popular item.

Screen Shot 2017-07-05 at 10.09.13 PM.png

Lets find out the top item for each department. We can see that Blueberries is the most popular item for frozen department and Extra Virgin Olive Oil is the most popular item for pantry department. Some unexpected surprises are dried mango, cotton swabs, and honey nut cheerios.

Screen Shot 2017-07-10 at 10.18.57 PM

We are also interested in knowing the reorder interval, how many days since the prior order before making the next order.

Screen Shot 2017-07-05 at 11.47.04 PM.png

As we discovered, 30 days is the most frequent reorder interval. It looks like most of customers reorder once a month. On the other hand, 320,608 orders were placed after 7 days since the prior order. We can concluded that majority of customers reorder after 1 month or 1 week since the prior order.

Stay tuned for next blog on my study results at the individual user level.

Scala Enumeration

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In Java we use enum to represent fixed set of constants

For example, we would define days of week enum type as follows

public enum Day {
    SUNDAY, MONDAY, TUESDAY, WEDNESDAY,
    THURSDAY, FRIDAY, SATURDAY 
}

In Scala, we can do the same thing by extending Enumeration, for example

object Day extends Enumeration {
  type Day = Value
  val SUNDAY, MONDAY, TUESDAY, WEDNESDAY, THURSDAY, FRIDAY, SATURDAY = Value
}

You can find examples of Scala Enumeration usage in Spark
https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/TaskState.scala

https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala

https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/executor/InputMetrics.scala