Sifarish is a suite of recommendation engines implementaed on Hadoop and Storm. Various algorithms, including feature similarity based recommendation and collaborative filtering based recommendation using social rating data will be available

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/

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. 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.

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

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

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

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 map reduce jobs. Some of them are optional. Please refer to the tutorial document tutorial.txt

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.

There is suppoort for structured fields e.g., Location, Time Window, Categorized Item, Product etc. These provide contextual dimensions to recommendation. They are particularly relevant for recommendation in the mobile space

Based on recent work in this area, I am working on implementing some algorithms to introduce
diversity and novelty in recommendation

For content based recommendation, faceted match is supported as faceted search in Solr. Faceted fields are specified through a configuration parameter

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.

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