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https://github.com/pranab/sifarish

Content based and collaborative filtering based recommendation and personalization engine implementation on Hadoop and Storm
https://github.com/pranab/sifarish

Last synced: 24 days ago
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Content based and collaborative filtering based recommendation and personalization engine implementation on Hadoop and Storm

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README 

          
## Introduction

Sifarish is a suite of solutions for recommendation personalization implementaed on 

Hadoop and Storm. Various  algorithms, including  feature similarity based recommendation 

and collaborative filtering based recommendation using social rating data are available



## Philosophy

* Providing complete business solutions, not just bunch of machine learning algorithms

* Simple to use

* Input output in CSV format

* Metadata defined in simple JSON file

* Extremely configurable with tons of configuration knobs



## Getting Started

Please read ../resource/GentleIntroductionToSifarish.docx for a high level introduction

and overview. The various tutorial documents in the resource directory are useful for

running different example use cases.



## Blogs

The following blogs of mine are good source of details of sifarish. These are the only source

of detail documentation



* http://pkghosh.wordpress.com/2011/10/26/similarity-based-recommendation-basics/

* http://pkghosh.wordpress.com/2011/11/28/similarity-based-recommendation-hadoop-way/

* http://pkghosh.wordpress.com/2011/12/15/similarity-based-recommendation-text-analytic/

* http://pkghosh.wordpress.com/2012/04/21/socially-accepted-recommendation/

* http://pkghosh.wordpress.com/2010/10/19/recommendation-engine-powered-by-hadoop-part-1/

* http://pkghosh.wordpress.com/2010/10/31/recommendation-engine-powered-by-hadoop-part-2/

* http://pkghosh.wordpress.com/2012/12/31/get-social-with-pearson-correlation/

* http://pkghosh.wordpress.com/2012/09/03/from-item-correlation-to-rating-prediction/

* http://pkghosh.wordpress.com/2014/02/10/from-explicit-user-engagement-to-implicit-product-rating/

* http://pkghosh.wordpress.com/2014/04/14/making-recommendations-in-real-time/

* http://pkghosh.wordpress.com/2014/05/26/popularity-shaken/

* http://pkghosh.wordpress.com/2014/06/23/novelty-in-personalization/

* http://pkghosh.wordpress.com/2014/09/10/realtime-trending-analysis-with-approximate-algorithms/

* http://pkghosh.wordpress.com/2014/12/22/positive-feedback-driven-recommendation-rank-reordering/

* https://pkghosh.wordpress.com/2015/01/20/diversity-in-personalization-with-attribute-diffusion/

* https://pkghosh.wordpress.com/2015/03/22/customer-service-and-recommendation-system/



## Content Similarity Based Recommendation

In the absence of social rating data, the only options is a feature similarity 

based recommendation. Similarity is calculated based on distance between entities 

in a multi dimensional feature space. Some examples are - recommending jobs based 

on user's resume - recommending products based on user profile. These

solutions are known as content based recommendation, because it's based innate 

features of some entity.



There are two different solutions as follows

1. Similarity between entities of different types (e.g. user profile and product)

2. Similarity between entities of same type (e.g. product)



Attribute meta data is defined in a json file. Both entities need not have the 

same set of attributes. Mapping between attributes values from one entity to 

the other can be defined in the config file.



The data type supported are numerical (integer), categorical, text, geo location, time. The 

distance algorithms  can be chosed to be euclidian, manhattan or minkowski. The default algorithm 

is euclidian. 



The distancs between different atrributes of different types are combined to find distance between 

two entity instances. Different weights can be assigned to the attributes to control the relative 

importance of different attributes.



The tutorial ../resource/product_similarity_tutorial.txt is a good starting point. The relevant

blogs are useful to understand the inner workings.



## Social Interaction Data Based Recommendation

These solutions are based user behavior data with respect to some product 

or service. these algorithms are also known as collaborative filtering.  



User behavior data is defined in terms of some explicit rating by user 

or it's derived from user  behavior in the site. The essential  input to all these algorithms 

is a matrix of user and items. The value for a cell could be the ratingas an integer. It could 

also be boolean,  if the user's interest in an item is expressed as a boolean



The tutorial ../resource/tutorial.txt is a good starting point. The relevant blogs are useful 

to understand the inner workings.



## Cold Starting Recommenders

These solutions are used when enough social data is not avaialable. 



1. If data contains text attributes, use TextAnalyzer MR to convert text to token stream 

   using lucene

2. Find similar items based on user profile. Use DiffTypeSimilarity MR

3. Use TopMatches MR to find top n matches for a profile



## Warm Starting Recommenders

When limited amount of user behavior data is available, these solutuions are appropriate



1. If data contains text attributes, use TextAnalyzer MR to convert text to token stream 

   using lucene

2. Find similar items by pairing items with one another using SameTypeSimilarity MR

3. Use TopMatches MR to find top n matches for a product



## Recommenders with Fully engaged Users

When significant of user behavior data is available, these soltions can be used. In 

the order of  complexity, the choices are as follows. They are all based on social data



There two phases for collaborative filetering based recommendation using social data

1. Find correlation between items 2. Predict rating based on items alreadyv rated and 

result of 1



The process involved running multiple map reduce jobs. Some of them are optional. Please refer to the 

tutorial document tutorial.txt in the resource directory



## Real Time Recommendation

Recommendations can be made real time based on user's current behavior in a pre defined time

window. The solution is based on Storm, although Hadoop gets used to compute item correlation

matrix from historical user behavior data.



## Text Attribute

For content based recommendation being able to find match between text field is an important

factor. Text attributes are stemmed or normalized with Apache Lucene. Various languages, in addition

to default of english are supported. They are german, french, italian, spanish, polish and brazilian

portuguese. Text matching algorithms supported are cosine, jaccard and semantic. For semantic matching,

RDF semantic graph is used 



## Complex Attributes

For content based recommendation, There is  support for structured fields e.g., Location, Time Window, 

Event, Categorized Item etc. Many of these  provide contextual dimensions to recommendation. They 

are particularly relevant for recommendation in the mobile space



## Novelty 

Novelty for an item can be computed at individual user level or the whole user community as a

whole. Novelty is blended into the final recommendation list by taking weighted average of

predicted rating and novelty  



## Diversilty 

Based on recent work in the academic world, I am working on implementing some  algorithms to introduce  

diversity in recommendation. Unlike novelty, diversity is group wise property. Diversity can be

defined either in terms item dissimilarity in a collaborative filtering sense or structural and content 

sense



## Facted Match

For content based recommendation, faceted match is supported as faceted search in Solr.

Faceted fields are specified through a configuration parameter



## Dithering

Dithering effectively handles the problem users usually not browsing the first few items

in a list. The dithering process shuffles the list little bit, every time recommended items 

are presented to the user.

 

## Getting started

Please use the tutorial.txt file in the resource directory for batch mode recommendation 

processing. For real time recommendation please use the tutorial document there is a separate

tutorial document realtime\_recommendation\_tutorial.txt



## Integration with other recommndation systems

If you use Apache mahout or some thing else for recommendation, you can

bring your basic recommendation output (userID, itemID, predictedRating) to

sifarish for additional postprocessing to improve the quality of the output. They are

listed in the next section.



## Post processing plugins

Just accuracy from the CF algorithm is not enough for a good recommender. There

are various post processing plugins are essential. They improve the quality of results. 

Here is the list. Sifarsh supports most them. Some are under development.



* Business goal injection 

* Adding novelty 

* Adding diversity 

* Rank reordering for explicit positive feedback 

* Rank reordering for implicit negative feedback 

* Dithering



## Configuration

Please refer to the wiki page for a detailed list of all configuration parameters

https://github.com/pranab/sifarish/wiki/Configuration. Going through the tutorial documents

in the resource directory, you can find sample configuration for various use cases.



## Build

Please read jar\_dependency.txt in the resource directory for build and run time dependency



For Hadoop 1

* mvn clean install



For Hadoop 2 (non yarn), use the branch nuovo

* git checkout nuovo

* mvn clean install



For Hadoop 2 (yarn), use the branch nuovo

* git checkout nuovo

* mvn clean install -P yarn



## Help

Please feel free to email me at pkghosh99@gmail.com



## Contribution

Contributors are welcome. Please email me at pkghosh99@gmail.com