论文标题
在推荐系统中生成自我启示性偏好，以解决冷启动问题
Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems
论文作者
论文摘要
当用户遭受熟悉，重复甚至是可预测的建议时，经典的面向准确性的推荐系统（RSS）通常会面临冷启动的问题和过滤器问题，使其无聊且不满意。为了解决上述问题，提议以串行性为导向的RSS推荐吸引人和有价值的物品与用户的历史互动显着偏离，从而通过向他们引入未探索但相关的候选物品来满足它们。在本文中，我们设计了一个新颖的面向串行的推荐系统（\ textbf {g}势力\ textbf {s} elf- \ textbf {s} erendipity \ textbf {r textbf {r} ecommender \ ecommender \ ecommender \ textbf \ textbf {s} ystem，\ textbf'seft \ textbf''提高建议性能。具体而言，该模型提取用户的兴趣和满意度偏好，从自己中产生虚拟但有说服力的邻居偏好，并实现他们的自我偏爱。然后将这些偏好注入额定矩阵中，作为RS模型的其他信息。请注意，GS $^2 $ -RS不仅可以解决冷启动问题，而且还提供了不同但相关的建议，以减轻过滤器的问题。基准数据集上的广泛实验表明，提出的GS $^2 $ -RS模型可以在偶然性度量中的最新基线方法大大优于具有稳定的精度性能的最新基线方法。
Classical accuracy-oriented Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble problem when users suffer the familiar, repeated, and even predictable recommendations, making them boring and unsatisfied. To address the above issues, serendipity-oriented RSs are proposed to recommend appealing and valuable items significantly deviating from users' historical interactions and thus satisfying them by introducing unexplored but relevant candidate items to them. In this paper, we devise a novel serendipity-oriented recommender system (\textbf{G}enerative \textbf{S}elf-\textbf{S}erendipity \textbf{R}ecommender \textbf{S}ystem, \textbf{GS$^2$-RS}) that generates users' self-serendipity preferences to enhance the recommendation performance. Specifically, this model extracts users' interest and satisfaction preferences, generates virtual but convincible neighbors' preferences from themselves, and achieves their self-serendipity preference. Then these preferences are injected into the rating matrix as additional information for RS models. Note that GS$^2$-RS can not only tackle the cold-start problem but also provides diverse but relevant recommendations to relieve the filter-bubble problem. Extensive experiments on benchmark datasets illustrate that the proposed GS$^2$-RS model can significantly outperform the state-of-the-art baseline approaches in serendipity measures with a stable accuracy performance.
