中国科学院自动化研究所机构知识库

Recommender systems are being widely applied in many application settings to suggest products,
services, and information items to potential consumers. Collaborative filtering, the most success-
ful recommendation approach, makes recommendations based on past transactions and feedback
from consumers sharing similar interests. A major problem limiting the usefulness of collaborative
filtering is the sparsity problem, which refers to a situation in which transactional or feedback
data is sparse and insufficient to identify similarities in consumer interests. In this article, we pro-
pose to deal with this sparsity problem by applying an associative retrieval framework and related
spreading activation algorithms to explore transitive associations among consumers through their
past transactions and feedback. Such transitive associations are a valuable source of information
to help infer consumer interests and can be explored to deal with the sparsity problem. To evalu-
ate the effectiveness of our approach, we have conducted an experimental study using a data set
from an online bookstore. We experimented with three spreading activation algorithms including
a constrained Leaky Capacitor algorithm, a branch-and-bound serial symbolic search algorithm,
and a Hopfield net parallel relaxation search algorithm. These algorithms were compared with
several collaborative filtering approaches that do not consider the transitive associations: a simple
graph search approach, two variations of the user-based approach, and an item-based approach.
Our experimental results indicate that spreading activation-based approaches significantly out-
performed the other collaborative filtering methods as measured by recommendation precision,
recall, the F-measure, and the rank score. We also observed the over-activation effect of the spread-
ing activation approach, that is, incorporating transitive associations with past transactional data
that is not sparse may “dilute” the data used to infer user preferences and lead to degradation in
recommendation performance.