Recently, the interest of graph representation learning has been rapidly
increasing in recommender systems. However, most existing studies have focused
on improving accuracy, but in real-world systems, the recommendation diversity
should be considered as well to improve user experiences. In this paper, we
propose the diversity-emphasized node embedding div2vec, which is a random
walk-based unsupervised learning method like DeepWalk and node2vec. When
generating random walks, DeepWalk and node2vec sample nodes of higher degree
more and nodes of lower degree less. On the other hand, div2vec samples nodes
with the probability inversely proportional to its degree so that every node
can evenly belong to the collection of random walks. This strategy improves the
diversity of recommendation models. Offline experiments on the MovieLens
dataset showed that our new method improves the recommendation performance in
terms of both accuracy and diversity. Moreover, we evaluated the proposed model
on two real-world services, WATCHA and LINE Wallet Coupon, and observed the
div2vec improves the recommendation quality by diversifying the system.

本研究提出了基于随机游走的无监督学习方法，即面向推荐系统的多样性强调节点嵌入 div2vec，在离线实验和两项真实世界服务中得出，div2vec 方法通过提高 recommendation diversity，提高了推荐性能，包括准确性和多样性。

div2vec: 强调多样性的节点嵌入

div2vec: Diversity-Emphasized Node Embedding

Recommender systems are often biased toward popular items. In other words,
few items are frequently recommended while the majority of items do not get
proportionate attention. That leads to low coverage of items in recommendation
lists across users (i.e. low aggregate diversity) and unfair distribution of
recommended items. In this paper, we introduce FairMatch, a general graph-based
algorithm that works as a post-processing approach after recommendation
generation for improving aggregate diversity. The algorithm iteratively finds
items that are rarely recommended yet are high-quality and add them to the
users' final recommendation lists. This is done by solving the maximum flow
problem on the recommendation bipartite graph. While we focus on aggregate
diversity and fair distribution of recommended items, the algorithm can be
adapted to other recommendation scenarios using different underlying
definitions of fairness. A comprehensive set of experiments on two datasets and
comparison with state-of-the-art baselines show that FairMatch, while
significantly improving aggregate diversity, provides comparable recommendation
accuracy.

介绍一种针对推荐系统中存在的偏见的一般性的基于图的算法 FairMatch，可在推荐生成后作为后处理方法来提高推荐的多样性和公平性，通过在推荐二分图上解决最大流问题来找到很少推荐但却是高质量的项目并将其添加到用户的最终推荐列表中。

基于图的方法提高推荐系统中的聚合多样性 ——FairMatch

FairMatch: A Graph-based Approach for Improving Aggregate Diversity in  Recommender Systems

Sequential Recommendation (SRs) that capture users' dynamic intents by
modeling user sequential behaviors can recommend closely accurate products to
users. Previous work on SRs is mostly focused on optimizing the recommendation
accuracy, often ignoring the recommendation diversity, even though it is an
important criterion for evaluating the recommendation performance. Most
existing methods for improving the diversity of recommendations are not ideally
applicable for SRs because they assume that user intents are static and rely on
post-processing the list of recommendations to promote diversity. We consider
both recommendation accuracy and diversity for SRs by proposing an end-to-end
neural model, called Intent-aware Diversified Sequential Recommendation (IDSR).
Specifically, we introduce an Implicit Intent Mining module (IIM) into SRs to
capture different user intents reflected in user behavior sequences. Then, we
design an Intent-aware Diversity Promoting (IDP) loss to supervise the learning
of the IIM module and force the model to take recommendation diversity into
consideration during training. Extensive experiments on two benchmark datasets
show that IDSR significantly outperforms state-of-the-art methods in terms of
recommendation diversity while yielding comparable or superior recommendation
accuracy.

提出了一种名为 IDSR 的端到端神经模型，包括一个隐式意图挖掘模块，可用于捕捉 SRs 中反映在用户行为序列中的不同用户意图，并设计了一个意图感知的多样性促进损失，以在训练期间考虑推荐多样性，从而在推荐多样性方面显着优于其他最先进的方法，并在推荐准确性方面取得可比或更高的性能。