Mahout is a collection of machine learning algorithms intended to perform the following operations as recommendation (Collaborative Filtering), Clustering and Classification. Initially to implement recommendation we need an input data file where every line contains one record each. Each record should have the user ID, Item ID and preference value in order separated by comma.

 

Input File – input.txt

501,1002,5

501,1012,3

510,1002,2

515,1002,5

501,1020,1

…

                The point to be considered here that we need the User Id and Item ID to be integers, alpha numeric characters won’t serve our purpose. Also the larger the input files better the quality of recommendations produced

 

                Recommenders are broadly classified into two categories based on the method or approach they use in generating recommendations

1.       User Based Recommendations

                Recommendations are derived from how similar items are to items, ie based on the items a user has already more similar items are recommended

 

2.       Item Based Recommendations

                Recommendations are derived on how similar users to users are. ie to make recommendations for a user(User1) we take into account an user/users who shares similar tastes and based on the items they possess we recommend items to User1

 

 

                When we make mahout recommendations the key components involved are

                It is an encapsulation used by Mahout to hold input data. It helps efficient access to data by various recommender algorithms.

                There are various kind of Similarity algorithms available and mahout has implementations of all the popular ones like Person Correlation, Cosine Measure, Euclidean Distance, Log Likelihood, Tanimoto coefficient etc

                This is applicable for user based recommendations, user based recommendations are made based on user to user similarity. We form a neighborhood of most similar users that share almost same tastes so that we get better recommendations. And the algorithms thet we use to select user neighborhood are

1.       Nearest N User Neighborhood

                Here we specify the neighborhood size, ie exactly the number of most similar uses to be considered for generating recommendations say 100,500 etc

2.       Threshold User Neighborhood

                We don’t specify the neighborhood size, rather we specify a similarity measure which is a value between -1 and +1. If we specify a value .7 then only the users that share a similarity greater than ).7 would be considered in neighborhood. Higher the value more similar the users are

                It is the final computing object which couples together the datamodel, similarity algorithm and neighborhood to generate recommendations based on the same

 

                Samples code snippets to generate user and item based recommendations are given below

 

 

 

 

 

 

Note: To get some recommendations you a sufficiently large input file. A few lines of input won’t gain you any recommendations