当前位置: X-MOL 学术arXiv.cs.IR › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Learning Complex Users' Preferences for Recommender Systems
arXiv - CS - Information Retrieval Pub Date : 2021-07-04 , DOI: arxiv-2107.01529
Shahpar Yakhchi

Recommender systems (RSs) have emerged as very useful tools to help customers with their decision-making process, find items of their interest, and alleviate the information overload problem. There are two different lines of approaches in RSs: (1) general recommenders with the main goal of discovering long-term users' preferences, and (2) sequential recommenders with the main focus of capturing short-term users' preferences in a session of user-item interaction (here, a session refers to a record of purchasing multiple items in one shopping event). While considering short-term users' preferences may satisfy their current needs and interests, long-term users' preferences provide users with the items that they may interact with, eventually. In this thesis, we first focus on improving the performance of general RSs. Most of the existing general RSs tend to exploit the users' rating patterns on common items to detect similar users. The data sparsity problem (i.e. the lack of available information) is one of the major challenges for the current general RSs, and they may fail to have any recommendations when there are no common items of interest among users. We call this problem data sparsity with no feedback on common items (DSW-n-FCI). To overcome this problem, we propose a personality-based RS in which similar users are identified based on the similarity of their personality traits.

中文翻译:

学习复杂用户对推荐系统的偏好

推荐系统 (RS) 已成为非常有用的工具,可帮助客户进行决策过程、找到他们感兴趣的项目并缓解信息过载问题。RS 中有两种不同的方法:(1) 以发现长期用户偏好为主要目标的一般推荐,以及 (2) 主要关注在一个会话中捕获短期用户偏好的顺序推荐。用户-商品交互(这里,会话是指在一次购物活动中购买多件商品的记录)。在考虑短期用户的偏好可能满足他们当前的需求和兴趣的同时,长期用户的偏好为用户提供他们最终可能与之交互的项目。在本论文中,我们首先关注提高通用 RS 的性能。大多数现有的通用 RS 倾向于利用用户对常见项目的评分模式来检测相似用户。数据稀疏问题(即缺乏可用信息)是当前通用 RS 面临的主要挑战之一,当用户之间没有共同感兴趣的项目时,它们可能无法提供任何建议。我们将此问题称为数据稀疏性,对常见项目没有反馈 (DSW-n-FCI)。为了克服这个问题,我们提出了一种基于个性的 RS,其中根据他们的个性特征的相似性来识别相似的用户。当用户之间没有共同感兴趣的项目时,他们可能无法提供任何建议。我们将此问题称为数据稀疏性,对常见项目没有反馈 (DSW-n-FCI)。为了克服这个问题,我们提出了一种基于个性的 RS,其中根据他们的个性特征的相似性来识别相似的用户。当用户之间没有共同感兴趣的项目时,他们可能无法提供任何建议。我们将此问题称为数据稀疏性,对常见项目没有反馈 (DSW-n-FCI)。为了克服这个问题,我们提出了一种基于个性的 RS,其中根据他们的个性特征的相似性来识别相似的用户。
更新日期:2021-07-06
down
wechat
bug