  Recommendation from implicit feedback is a highly challenging task due to the
lack of reliable negative feedback data. Existing methods address this
challenge by treating all the un-observed data as negative (dislike) but
downweight the confidence of these data. However, this treatment causes two
problems: (1) Confidence weights of the unobserved data are usually assigned
manually, which lack flexibility and may create empirical bias on evaluating
user's preference. (2) To handle massive volume of the unobserved feedback
data, most of the existing methods rely on stochastic inference and data
sampling strategies. However, since a user is only aware of a very small
fraction of items in a large dataset, it is difficult for existing samplers to
select informative training instances in which the user really dislikes the
item rather than does not know it.
  To address the above two problems, we propose two novel recommendation
methods SamWalker and SamWalker++ that support both adaptive confidence
assignment and efficient model learning. SamWalker models data confidence with
a social network-aware function, which can adaptively specify different weights
to different data according to users' social contexts. However, the social
network information may not be available in many recommender systems, which
hinders application of SamWalker. Thus, we further propose SamWalker++, which
does not require any side information and models data confidence with a
constructed pseudo-social network. We also develop fast random-walk-based
sampling strategies for our SamWalker and SamWalker++ to adaptively draw
informative training instances, which can speed up gradient estimation and
reduce sampling variance. Extensive experiments on five real-world datasets
demonstrate the superiority of the proposed SamWalker and SamWalker++.
