Category Archives: Recommendation Engine

Mining Seasonal Products from Sales Data

Posted on May 30, 2017 by Pranab

The other day someone asked me how to include products with seasonal demand in recommendations based on collaborative filtering or some other technique. The solution to the problem involves two steps. The first step is to identify products with seasonal … Continue reading →

Posted in Big Data, Data Mining, Data Science, eCommerce, Map Reduce, Recommendation Engine | Tagged , , , | Leave a comment

Customer Service and Recommendation System

Posted on March 22, 2015 by Pranab

You may be wondering about the relationship, I alluded to in the title. A personalization and recommendation system like sifarish bootstraps from user and item engagement data. This kind of data is gleaned from various signals e.g. an user’s engagement with … Continue reading →

Posted in Big Data, Customer Service, eCommerce, Hadoop and Map Reduce, Recommendation Engine | Tagged , | 1 Comment

Diversity in Personalization with Attribute Diffusion

Posted on January 20, 2015 by Pranab

One of the nagging problems in  personalized recommendation systems is crowding of items with same attribute values in the recommendation list. For example, if you happen to like certain artiste, the songs by the same artiste will tend to flood recommended … Continue reading →

Posted in Big Data, Data Science, Hadoop and Map Reduce, Personalization, Recommendation Engine | Tagged , | Leave a comment

Positive Feedback Driven Recommendation Rank Reordering

Posted on December 22, 2014 by Pranab

The basic recommendation output consisting of the tuple (user, item, predicted rating), is easy to obtain from any Collaborative Filtering (CF) based Recommendation and Personalization engine, including sifarish. It’s been reported that there is a bigger return for the quality of … Continue reading →

Posted in Big Data, Collaborative Filtering, Hadoop and Map Reduce, Personalization, Recommendation Engine | Tagged , , | 1 Comment

Location and Time Based Service

Posted on August 11, 2014 by Pranab

When I implemented feature similarity based matching engine in my open source Personalization and Recommendation Engine sifarish, it was for addressing the cold start problem. It allowed me to do content or feature based recommendation for users with limited engagement. … Continue reading →

Posted in Big Data, Hadoop and Map Reduce, Mobile, Real Time Processing, Recommendation Engine, Search, Spark, Storm | Tagged , , , | Leave a comment

Novelty in Personalization

Posted on June 23, 2014 by Pranab

We all have the unfortunate  experience of being pigeon holed by Personalization and Recommendation engines. When recommendation are based on our past behavior and there is very little  opportunity to explore. But our past actions are not always good predictors for … Continue reading →

Posted in Big Data, Data Mining, Data Science, Hadoop and Map Reduce, Personalization, Recommendation Engine | Tagged , | 1 Comment

Popularity Shaken

Posted on May 26, 2014 by Pranab

We will be addressing two important issues faced by recommendation systems. First, how do you solve the cold start problem i.e., provide recommendations for new users with very limited behavior data available. Second, even if we have a recommendation list for … Continue reading →

Posted in Big Data, Hadoop and Map Reduce, Recommendation Engine, Storm | Tagged , , | 3 Comments

Making Recommendations in Real Time

Posted on April 14, 2014 by Pranab

Making recommendations based on an user’s current behavior in a small time window is a powerful feature that has been added to sifarish recently. In this post I will go over the details of this feature. The real time feature … Continue reading →

Posted in Big Data, Collaborative Filtering, Data Mining, Data Science, Hadoop and Map Reduce, Real Time Processing, Recommendation Engine, Redis, Storm | Tagged , | 2 Comments

From Explicit User Engagement to Implicit Product Rating

Posted on February 10, 2014 by Pranab

The basic input for sifarish or any other collaborative filtering  based recommendation engine is user rating of items. However explicit  rating by users is not always available. Even when it’s available, it’s been known that generally only users with extreme … Continue reading →

Posted in Big Data, Data Science, eCommerce, Hadoop and Map Reduce, Recommendation Engine, Web Analytic | Tagged , , | 23 Comments

Business Goal Infused Recommendation

Posted on May 19, 2013 by Pranab

The output of a recommendation engine,  whether based on collaborative filtering or some other techniques reflects consumer’s interest in products or services. However a business may have some goals that may be at odds with the items recommended by  the … Continue reading →

Posted in Big Data, Collaborative Filtering, Hadoop and Map Reduce, Predictive Analytic, Recommendation Engine | Tagged , , | 1 Comment