  One key property in recommender systems is the long-tail distribution in
user-item interactions where most items only have few user feedback. Improving
the recommendation of tail items can promote novelty and bring positive effects
to both users and providers, and thus is a desirable property of recommender
systems. Current novel recommendation studies over-emphasize the importance of
tail items without differentiating the degree of users' intent on popularity
and often incur a sharp decline of accuracy. Moreover, none of existing methods
has ever taken the extreme case of tail items, i.e., cold-start items without
any interaction, into consideration.
  In this work, we first disclose the mechanism that drives a user's
interaction towards popular or niche items by disentangling her intent into
conformity influence (popularity) and personal interests (preference). We then
present a unified end-to-end framework to simultaneously optimize accuracy and
novelty targets based on the disentangled intent of popularity and that of
preference. We further develop a new paradigm for novel recommendation of
cold-start items which exploits the self-supervised learning technique to model
the correlation between collaborative features and content features. We conduct
extensive experimental results on three real-world datasets. The results
demonstrate that our proposed model yields significant improvements over the
state-of-the-art baselines in terms of accuracy, novelty, coverage, and
trade-off.
