arXiv:2607.02539cs.SIcs.CL2026-07

研究发现满意度指标不能准确反映用户真实幸福感,建议推荐系统关注更深层心理福祉。

Beyond Satisfaction: Learning Associations Between Content, Reviews, and Well-Being

论文配图:Beyond Satisfaction: Learning Associations Between Content, Reviews, and Well-Being
图 1 · 摘自论文原文
  • 通过分析书籍评分与评论内容,探索满意度与心理福祉的关联机制。
  • 评分和情感仅与短期享乐型幸福相关,与长期意义感关联弱。
  • 涉及价值观、宗教等主题的内容更促进深层幸福感,适合关注心理健康的推荐系统参考。

数字平台通常以评分、点赞和情感倾向等满意度信号优化内容推荐,隐含将满意度视为用户福祉的代理指标。然而,心理学理论认为福祉是多维度的,超越满意度或短期积极情绪。本文聚焦书籍消费场景,研究满意度信号是否真正反映福祉,以及哪些内容类型与不同福祉维度相关。结果显示:(a)评分与情感仅与心理福祉多数维度弱相关;(b)它们更贴近即时享乐型福祉,而非持久的意义型福祉。通过将评论内容与文本主题关联分析,发现涉及价值观、宗教(皮尔逊相关 r=0.35)或人类动机(r=0.26)的主题分别与更高意义感和成就感相关;而不文明语言(平均 r=-0.15)及过去导向表达(平均 r=-0.12)则与较低福祉相关。这些发现提示推荐系统应超越满意度,采用更丰富的福祉目标。

原文摘要 · Abstract (English)

Digital platforms commonly optimize for satisfaction using signals such as ratings, likes, and sentiment, implicitly treating satisfaction as a proxy for user well-being. Psychological theory, however, characterizes well-being as a multidimensional construct that extends beyond satisfaction or short-term positivity. In this paper, we examine whether commonly used satisfaction signals capture expressions of well-being, and what types of content are associated with different well-being outcomes. Our study focuses on book consumption, a convenient domain wherein users engage substantially with fixed pieces of content and sometimes provide nuanced long-form feedback. Our results show that (a) rating scales and sentiment only loosely correlate with most facets of psychological well-being and (b) ratings and sentiment are more closely aligned with immediate and hedonic as opposed to enduring and eudaimonic expressions of well-being. Further, by linking reviews to book content, we find that themes related to values, and institutions like religion (Pearson r = 0.35) or human drives (r=.26) are associated with higher meaning and accomplishment respectively, while incivility (avg r = -.15) and past-focused language (avg r = -.12) are associated with lower well-being. These findings motivate richer outcome targets for content recommendation systems beyond satisfaction alone.

心理福祉推荐系统内容分析

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