  Media recommender systems aim to capture users' preferences and provide
precise personalized recommendation of media content. There are two critical
components in the common paradigm of modern recommender models: (1)
representation learning, which generates an embedding for each user and item;
and (2) interaction modeling, which fits user preferences towards items based
on their representations. In spite of great success, when a great amount of
users and items exist, it usually needs to create, store, and optimize a huge
embedding table, where the scale of model parameters easily reach millions or
even larger. Hence, it naturally raises questions about the heavy recommender
models: Do we really need such large-scale parameters? We get inspirations from
the recently proposed lottery ticket hypothesis (LTH), which argues that the
dense and over-parameterized model contains a much smaller and sparser
sub-model that can reach comparable performance to the full model. In this
paper, we extend LTH to media recommender systems, aiming to find the winning
tickets in deep recommender models. To the best of our knowledge, this is the
first work to study LTH in media recommender systems. With MF and LightGCN as
the backbone models, we found that there widely exist winning tickets in
recommender models. On three media convergence datasets -- Yelp2018, TikTok and
Kwai, the winning tickets can achieve comparable recommendation performance
with only 29%~48%, 7%~10% and 3%~17% of parameters, respectively.
