J.S. Kong, K. Teague
& J. Kessler; JMLR W&CP 18:249–261, 2012.
The Love-Hate Square Counting Method for Recommender Systems
Recommender systems provide personalized suggestions to users and are critical to the
success of many e-commerce sites, such as Netﬂix and Amazon. Outside of e-commerce,
recommender systems can be deployed in ﬁelds such as intelligence analysis, for recommending
high-quality information source to analysts for further examination. In this work, we present the
square counting method
for rating predictions in recommender systems. Our method is based on
analyzing the bipartite rating network with score-labeled edges representing user nodes’ ratings to
item nodes. Edges are denoted as an I-love-it
edge based on whether the rating score
on the edge is above or below a threshold. For a target user-item pair, we count the number for
each conﬁguration of love-hate squares that involve the target pair, where the sequence
edges determine the particular conﬁguration. The counts are
used as features in a supervised machine learning framework for training and rating
prediction. The method is implemented and empirically evaluated on a large-scale Yahoo!
music user-item rating dataset. Results show that the square counting method is fast,
simple to parallelize, scalable to massive datasets and makes highly accurate predictions.
Finally, we report an interesting empirical ﬁnding that conﬁgurations with consecutive
edges seem to provide the most powerful signal in predicting a user’s love for
an item. (Approved for public release by Northrop Grumman Information Systems,
ISHQ-2011-0042. The work was entirely performed when Kyle Teague was at Northrop
Page last modified on Tue May 29 10:23:41 2012.