Category Archives: Collaborative Filtering

Positive Feedback Driven Recommendation Rank Reordering

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

Making Recommendations in Real Time

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

Business Goal Infused Recommendation

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

Get Social with Pearson Correlation

In one of my earlier posts, I discussed about using Pearson correlation for making social recommendation. In this post we will delve deeper into it including the Hadoop map reduce implementation. There are many correlation techniques, including cosine distance, slope … Continue reading

Posted in Collaborative Filtering, Hadoop and Map Reduce, Predictive Analytic, Recommendation Engine | Tagged , | 3 Comments

Socially Accepted Recommendation

All my earlier posts on recommendation  systems focused on the so called content based recommendation. These systems rely on finding similarities between the attributes of entities  e.g., between products. They are useful to address the so called cold start problem, … Continue reading

Posted in Collaborative Filtering, Hadoop and Map Reduce, Predictive Analytic | Tagged , , | 1 Comment

Recommendation Engine Powered by Hadoop (Part 2)

In Part 1 of this post the focus was on finding the correlation between items, based on rating data available in individual items. The MR job output was the correlation coefficient matrix, with correlation coefficient  values between 0 and 1 … Continue reading

Posted in Collaborative Filtering, Data Mining, Hadoop and Map Reduce, Java | Tagged , , | 10 Comments

Recommendation Engine Powered by Hadoop (Part 1)

Personalized recommendations are ubiquitous in social network and shopping sites these days. How do they do it? Al long as enough user interaction data is available for items e.g., products in shopping sites, a kind of recommendation engine based on … Continue reading

Posted in Collaborative Filtering, Data Mining, Hadoop and Map Reduce | Tagged , , | 25 Comments