基于 ruby/redis 的推薦引擎：recommendify

recommendify 是基于 ruby/redis 的推薦引擎  (協同過濾)。

# 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") 

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