How to solve product recommendation issue like: User __bought__ XXX also __viewed__ YYY

Question

I am currently learning recommender system, learned something about collaborative filtering, User CF, Item CF, it is obvious to use these algorithm to solve problem like: 1) User bought XXX also bought YYY 2) User viewed XXX also viewed YYY

My question is: how to solve problem like: 1) User bought XXX also viewed YYY 2) User viewed XXX also bought YYY ?

Update: Just corrected the title to: " User bought XXX also viewed YYY"

Answer 1

While I am not sure this is really "recommendation", I can tell you how you'd approach recommendations across domains in Mahout. You would build two DataModels, one built on user-item purchases and one built on user-item views. You would use the purchase data as the input to a UserSimilarity or ItemSimilarity implementation, but, then feed the view data as the input DataModel to the Recommender implementation. You would then be computing something more like what you suggest.

Answer 2

Look at Taste framework. For more alternatives, check this post Recommendation Engines for Java applications

Answer 3

Say you have two tables products and sold_products. Each time you sell a product it gets added to the sold_products table. We will say the two tables are related by product_id, order_id is used to group orders together in sold_products.

We will assume the product you are looking at has a product_id of 1234.

1. Get a list of order_ids from the last 25 orders which contain the product.

SELECT DISTINCT sold_products.order_id FROM sold_products WHERE product_id=1234 LIMIT 25

1. From there we will put all the ids into a string separated by comers

e.g. PO1234,PO435,PO3456....

1. Select the product ids from those orders and I like to rank by frequency

SELECT DISTINCT products.* FROM sold_products LEFT JOIN products on products.product_id=sold_products.product_id WHERE sold_products.order_id IN (PO1234,PO435,PO3456....) AND NOT sold_products.product_id=1234 GROUP BY sold_products.product_id ORDER BY COUNT(1) DESC

Answer 4

You need to refer Chapter 2 of OReilly's 'Programming Collective Intelligence' book. To come up with matching products ie., 'Customer who bought this item also bought...' section, you need to

• first collect preferences of various users
• Then find similar users
• Then see other items they purchased or liked.

There are algorithms involved in above steps. More details are given in that book along with python code for those algorithms.

Answer 5

You would generally need two dataset. I .e transaction id & product as first & visitorID & productsviewed as second to arrive at a % of confidence of having any two products being sold(or viewed) together. You can use R (statistic software) & install a package called "arules" to generate these recommendations easily.

Here is a sample code that you may want to check out in R

setwd(“C:/Documents and Settings/rp/Desktop/output”); install.packages(“arules”); library(“arules”); txn = read.transactions(file=”Transactions_sample.csv”, rm.duplicates= FALSE, format=”single”,sep=”,”,cols =c(1,2)); basket_rules <- apriori(txn,parameter = list(sup = 0.5, conf = 0.9,target=”rules”)); inspect(basket_rules);

If you would really want to understand how it works, you may want to check out the white paper at http://www.tatvic.com/resources named as product purchase pattern analysis which indicates how you can do it simply with your web data.

Further, if you want to use a readymade API for it, it is available at http://www.liftsuggest.com/how-lift-product-recommendation-works