Over the past decades, for One-Class Collaborative Filtering (OCCF), many
learning objectives have been researched based on a variety of underlying
probabilistic models. From our analysis, we observe that models trained with
different OCCF objectives capture distinct aspects of user-item relationships,
which in turn produces complementary recommendations. This paper proposes a
novel OCCF framework, named ConCF, that exploits the complementarity from
heterogeneous objectives throughout the training process, generating a more
generalizable model. ConCF constructs a multi-branch variant of a given target
model by adding auxiliary heads, each of which is trained with heterogeneous
objectives. Then, it generates consensus by consolidating the various views
from the heads, and guides the heads based on the consensus. The heads are
collaboratively evolved based on their complementarity throughout the training,
which again results in generating more accurate consensus iteratively. After
training, we convert the multi-branch architecture back to the original target
model by removing the auxiliary heads, thus there is no extra inference cost
for the deployment. Our extensive experiments on real-world datasets
demonstrate that ConCF significantly improves the generalization of the model
by exploiting the complementarity from heterogeneous objectives.

这篇文章介绍了一个名为 ConCF 的新型 OCCF 框架，它利用不同的学习目标和概率模型，生成一个更具通用性的模型，通过构建多分支变体和引入辅助头部来达成共识，并通过协同进化提高了模型的泛化性能。

异构目标的一类协同过滤共识学习

Consensus Learning from Heterogeneous Objectives for One-Class  Collaborative Filtering

The goal of one-class collaborative filtering (OCCF) is to identify the
user-item pairs that are positively-related but have not been interacted yet,
where only a small portion of positive user-item interactions (e.g., users'
implicit feedback) are observed. For discriminative modeling between positive
and negative interactions, most previous work relied on negative sampling to
some extent, which refers to considering unobserved user-item pairs as
negative, as actual negative ones are unknown. However, the negative sampling
scheme has critical limitations because it may choose "positive but unobserved"
pairs as negative. This paper proposes a novel OCCF framework, named as BUIR,
which does not require negative sampling. To make the representations of
positively-related users and items similar to each other while avoiding a
collapsed solution, BUIR adopts two distinct encoder networks that learn from
each other; the first encoder is trained to predict the output of the second
encoder as its target, while the second encoder provides the consistent targets
by slowly approximating the first encoder. In addition, BUIR effectively
alleviates the data sparsity issue of OCCF, by applying stochastic data
augmentation to encoder inputs. Based on the neighborhood information of users
and items, BUIR randomly generates the augmented views of each positive
interaction each time it encodes, then further trains the model by this
self-supervision. Our extensive experiments demonstrate that BUIR consistently
and significantly outperforms all baseline methods by a large margin especially
for much sparse datasets in which any assumptions about negative interactions
are less valid.

本文提出了一种名为 BUIR 的新型 OCCF 框架，在没有负采样的情况下有效缓解了数据稀疏性问题，并利用随机数据增强来进一步提高模型性能。实验结果表明，BUIR 相比于已有的基线方法显著提高了推荐性能。

一类协同过滤的用户和物品表示的引导

Bootstrapping User and Item Representations for One-Class Collaborative  Filtering

Building a successful recommender system depends on understanding both the
dimensions of people's preferences as well as their dynamics. In certain
domains, such as fashion, modeling such preferences can be incredibly
difficult, due to the need to simultaneously model the visual appearance of
products as well as their evolution over time. The subtle semantics and
non-linear dynamics of fashion evolution raise unique challenges especially
considering the sparsity and large scale of the underlying datasets. In this
paper we build novel models for the One-Class Collaborative Filtering setting,
where our goal is to estimate users' fashion-aware personalized ranking
functions based on their past feedback. To uncover the complex and evolving
visual factors that people consider when evaluating products, our method
combines high-level visual features extracted from a deep convolutional neural
network, users' past feedback, as well as evolving trends within the community.
Experimentally we evaluate our method on two large real-world datasets from
Amazon.com, where we show it to outperform state-of-the-art personalized
ranking measures, and also use it to visualize the high-level fashion trends
across the 11-year span of our dataset.

本文介绍了一种利用深度卷积神经网络，结合用户反馈和时尚趋势进行实时热门商品推荐的算法，以及该算法在 Amazon 上进行的实验。