from ..base import FeatureRecommenderMixin
from ..model import OnlineSketch
import numpy as np
import numpy.linalg as ln
import scipy.sparse as sp
from sklearn import preprocessing
from sklearn.utils.extmath import safe_sparse_dot
class SketchRecommender(OnlineSketch, FeatureRecommenderMixin):
"""Online-matrix-sketching-based recommender
References
----------
- T. Kitazawa.
`Sketching Dynamic User-Item Interactions for Online Item Recommendation <http://dl.acm.org/citation.cfm?id=3022152>`_.
In *Proc. of CHIIR 2017*, March 2017.
"""
def initialize(self):
super(SketchRecommender, self).initialize()
def register_user(self, user):
super(SketchRecommender, self).register_user(user)
def register_item(self, item):
super(SketchRecommender, self).register_item(item)
i_vec = item.encode(index=False, feature=True, vertical=True)
i_vec = sp.csr_matrix(i_vec)
if self.i_mat.size == 0:
self.i_mat = i_vec
else:
self.i_mat = sp.csr_matrix(sp.hstack((self.i_mat, i_vec)))
def update(self, e, batch_train=False):
y = e.encode(index=False, feature=True, context=True)
self.update_model(y)
def score(self, user, candidates, context):
# i_mat is (n_item_context, n_item) for all possible items
# extract only target items
i_mat = self.i_mat[:, candidates]
n_target = len(candidates)
# u_mat will be (n_user_context, n_item) for the target user
u_vec = np.concatenate((user.feature, context))
u_vec = np.array([u_vec]).T
u_mat = sp.csr_matrix(np.repeat(u_vec, n_target, axis=1))
# stack them into (p, n_item) matrix
Y = sp.vstack((u_mat, i_mat))
Y = self.proj.reduce(Y)
Y = sp.csr_matrix(preprocessing.normalize(Y, norm='l2', axis=0))
X = np.identity(self.k) - np.dot(self.U_r, self.U_r.T)
A = safe_sparse_dot(X, Y, dense_output=True)
return ln.norm(A, axis=0, ord=2)
def recommend(self, user, candidates, context):
scores = self.score(user, candidates, context)
return self.scores2recos(scores, candidates)
