Cool feature in Flipkart’s user reviews trying to match a review to a particular item attribute

September 14, 2013

The flipkart Product Management and Research team have come up with a cool idea of trying to match a user review for a particular item to a specific attribute of the item only. They call it product features users are talking about. 

Image

 

As you can see that they have identified operating systems, games, value for money and apps as the features for iphone. 

Now, based on a particular feature, you chose, you can see all the reviews that are clustered under that feature.

Image

And then, you can select a particular review and read that review in detail. 

Image

 

This is a real cool feature and will massively improve buyers experience. This will also in future lead the way for more granular recommendations. If flipkart knows what features in a product you are looking for, it can recommend you products which are good in that feature based on the recommendations of users who have used that feature. A strong case of collaborative filtering. Better recommendations in the future when they have a good data set and more money.

I thing this is a nice example, where the product management team and the research (NLP and machine learning) team have come together to bring out a new feature for flipkart.

What would be interesting to see, on how many other different products or categories is flipkart showing this feature.

For watches they are not.

Some other cool features on their website are, certified buyer reviews. This puts in more authenticity on the review and is held credible by the reader. They also write if there is a first time reviewer. 

 

Advertisements

Difference between Information Retrieval and Information Filtering

September 10, 2013

Information retrieval is about fulfilling immediate queries from a library of information available.

Example : you have a deal store containing 100 deals and a query comes from a user. You show the deals that are relevant to that query.

Information Filtering is about processing a stream of information to match your static set of likes, tastes and preferrences.

Example : a clipper service which reads all the news articles published today and serves you content that is relevant to you based on your likes and interests.