基于 ruby/redis 的推荐引擎：recommendify
jopen
10年前
recommendify 是基于 ruby/redis 的推荐引擎 (协同过滤)。
# 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")