Recommendify is a ruby/redis based recommendation engine - The recommendations can be updated/processed incrementally and on multiple hosts. The worker is implemented in plain ruby and native C.

• "Users that bought this product also bought..." from user_id--bought-->product_id pairs
• "Users that viewed this video also viewed..." from user_id--viewed-->video_id pairs
• "Users that like this venue also like..." from user_id--likes-->venue_id pairs

Your input data (the so called interaction-sets) should look like this:

# FORMAT A: user bought products (select buyerid, productid from sales group_by buyerid)
[user23] product5 produt42 product17
[user42] product8 produt16 product5

# FORMAT B: user watched video (this can be transformed to the upper representation with a map/reduce)
user3 -> video3
user6 -> video19
user3 -> video6
user1 -> video42

The output data will look like this:

# similar products based on co-concurrent buys
product5 => product17 (0.78), product8 (0.43), product42 (0.31)
product17 => product5 (0.36), product8 (0.21), product42 (0.18)

# similar videos based on co-concurrent views
video19 => video3 (0.93), video6 (0.56), video42 (0.34)
video42 => video19 (0.32), video3 (0.21), video6 (0.08)

You can add new interaction-sets to the processor incrementally, but the similarities for changed items have to be re-processed after new interactions were added. You can either re-process all items (recommender.process!) from time to time or keep track of the updates and only process the changed items (recommender.process_item!)

# Our similarity matrix, we calculate the similarity via co-concurrence 
# of products in "orders" using the jaccard similarity measure.
class MyRecommender < Recommendify::Base

  # store only the top fifty neighbors per item
  max_neighbors 50

  # define an input data set "order_items". we'll add "order_id->product_id"
  # pairs to this input and use the jaccard coefficient to retrieve a 
  # "customers that ordered item i1 also ordered item i2" statement and apply
  # the result to the item<->item similarity matrix with a weight of 5.0
  input_matrix :order_items,  
    # :native => true,
    :similarity_func => :jaccard,    
    :weight => 5.0

end

recommender = MyRecommender.new

# add `order_id->product_id` interactions to the order_item_sim input
# you can add data incrementally and call RecommendedItem.process! to update
# the similarity matrix at any time.
recommender.order_items.add_set("order1", ["product23", "product65", "productm23"])
recommender.order_items.add_set("order2", ["product14", "product23"])

# Calculate all elements of the similarity matrix
recommender.process!

# ...or calculate a specific row of the similarity matrix (a specific item)
# use this to avoid re-processing the whole matrix after incremental updates
recommender.process_item!("product65")

# retrieve similar products to "product23"
recommender.for("item23") 
  => [ <Recommendify::Neighbor item_id:"product65" similarity:0.23>, (...) ]

# remove "product23" from the similarity matrix and the input matrices. you should 
# do this if your items 'expire', since it will speed up the calculation
recommender.delete_item!("product23") 

Recommendify keeps an incrementally updated item x item matrix, the "co-concurrency matrix". This matrix stores the number of times that a combination of two items has appeared in an interaction/preferrence set. The co-concurrence counts are processed with a jaccard similarity measure to retrieve another item x item similarity matrix, which is used to find the N most similar items for each item. This is also called "Item-based Collaborative Filtering with binary ratings" (see Miranda, Alipio et al. [1])

1. Group the input user->item pairs by user-id and store them into interaction sets
2. For each item<->item combination in the interaction set increment the respective element in the co-concurrence matrix
3. For each item<->item combination in the co-concurrence matrix calculate the item<->item similarity
4. For each item store the N most similar items in the respective output set.

The maximum number of entries in the co-concurrence and similarity matrix is k(n) = (n^2)-(n/2), it grows O(n^2). However, in a real scenario it is very unlikely that all item<->item combinations appear in a interaction set and we use a sparse matrix which will only use memory for elemtens with a value > 0. The size of the similarity grows O(n).

After you have compiled the native worker, you can pass the :native => true option to the input_matrix. This speeds up processing by at least 10x.

cd ~/.rvm/gems/ruby-1.9.3-p0/gems/recommendify-0.2.2/
bundle exec rake build_native

These recommendations were calculated from 2,3mb "profile visit"-data (taken from www.talentsuche.de) - keep in mind that the recommender uses only visitor->visited data, it doesn't know the gender of a user.

full snippet: http://falbala.23loc.com/~paul/recommendify_out_1.html

Initially processing the 120.047 visitor_id->profile_id pairs currently takes around half an hour with the ruby-only implementation and ~130 seconds with the native/c implementation on a single core. It creates a 24.1mb hashtable in redis (with truncated user_rows a' max 100 items). In another real data set with very short user rows (purchase/payment data) it used only 3.4mb for 90k items with very good results. You can try this for yourself; the complete data and code is in doc/example.rb and doc/example_data.csv.

[1] Miranda C. and Alipio J. (2008). Incremental collaborative filtering for binary ratings (LIAAD - INESC Porto, University of Porto)

[2] George Karypis (2000) Evaluation of Item-Based Top-N Recommendation Algorithms (University of Minnesota, Department of Computer Science / Army HPC Research Center)

[3] Shiwei Z., Junjie W. Hui X. and Guoping X. (2011) Scaling up top-K cosine similarity search (Data & Knowledge Engineering 70)

Copyright (c) 2011 Paul Asmuth

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