  In recent years, complementary recommendation has received extensive
attention in the e-commerce domain. In this paper, we comprehensively summarize
and compare 34 representative studies conducted between 2009 and 2024. Firstly,
we compare the data and methods used for modeling complementary relationships
between products, including simple complementarity and more complex scenarios
such as asymmetric complementarity, the coexistence of substitution and
complementarity relationships between products, and varying degrees of
complementarity between different pairs of products. Next, we classify and
compare the models based on the research problems of complementary
recommendation, such as diversity, personalization, and cold-start.
Furthermore, we provide a comparative analysis of experimental results from
different studies conducted on the same dataset, which helps identify the
strengths and weaknesses of the research. Compared to previous surveys, this
paper provides a more updated and comprehensive summary of the research,
discusses future research directions, and contributes to the advancement of
this field.
