  Happiness computing based on large-scale online web data and machine learning
methods is an emerging research topic that underpins a range of issues, from
personal growth to social stability. Many advanced Machine Learning (ML) models
with explanations are used to compute the happiness online assessment while
maintaining high accuracy of results. However, domain knowledge constraints,
such as the primary and secondary relations of happiness factors, are absent
from these models, which limits the association between computing results and
the right reasons for why they occurred. This article attempts to provide new
insights into the explanation consistency from an empirical study perspective.
Then we study how to represent and introduce domain knowledge constraints to
make ML models more trustworthy. We achieve this through: (1) proving that
multiple prediction models with additive factor attributions will have the
desirable property of primary and secondary relations consistency, and (2)
showing that factor relations with quantity can be represented as an importance
distribution for encoding domain knowledge. Factor explanation difference is
penalized by the Kullback-Leibler divergence-based loss among computing models.
Experimental results using two online web datasets show that domain knowledge
of stable factor relations exists. Using this knowledge not only improves
happiness computing accuracy but also reveals more significative happiness
factors for assisting decisions well.
