Hesitation and Tolerance in Recommender Systems
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911448864653312 |
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| author | Zou, Kuan Sun, Aixin Ji, Yitong Zhang, Hao Wang, Jing Zhuohao Zhang Jiang, Xuemeng |
| author_facet | Zou, Kuan Sun, Aixin Ji, Yitong Zhang, Hao Wang, Jing Zhuohao Zhang Jiang, Xuemeng |
| contents | Users' interactions with recommender systems often involve more than simple acceptance or rejection. We highlight two overlooked states: hesitation, when people deliberate without certainty, and tolerance, when this hesitation escalates into unwanted engagement before ending in disinterest. Across two large-scale surveys (N=6,644 and N=3,864), hesitation was nearly universal, and tolerance emerged as a recurring source of wasted time, frustration, and diminished trust. Analyses of e-commerce and short-video platforms confirm that tolerance behaviors, such as clicking without purchase or shallow viewing, correlate with decreased activity. Finally, an online field study at scale shows that even lightweight strategies treating tolerance as distinct from interest can improve retention while reducing wasted effort. By surfacing hesitation and tolerance as consequential states, this work reframes how recommender systems should interpret feedback, moving beyond clicks and dwell time toward designs that respect user value, reduce hidden costs, and sustain engagement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09950 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hesitation and Tolerance in Recommender Systems Zou, Kuan Sun, Aixin Ji, Yitong Zhang, Hao Wang, Jing Zhuohao Zhang Jiang, Xuemeng Information Retrieval Users' interactions with recommender systems often involve more than simple acceptance or rejection. We highlight two overlooked states: hesitation, when people deliberate without certainty, and tolerance, when this hesitation escalates into unwanted engagement before ending in disinterest. Across two large-scale surveys (N=6,644 and N=3,864), hesitation was nearly universal, and tolerance emerged as a recurring source of wasted time, frustration, and diminished trust. Analyses of e-commerce and short-video platforms confirm that tolerance behaviors, such as clicking without purchase or shallow viewing, correlate with decreased activity. Finally, an online field study at scale shows that even lightweight strategies treating tolerance as distinct from interest can improve retention while reducing wasted effort. By surfacing hesitation and tolerance as consequential states, this work reframes how recommender systems should interpret feedback, moving beyond clicks and dwell time toward designs that respect user value, reduce hidden costs, and sustain engagement. |
| title | Hesitation and Tolerance in Recommender Systems |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2412.09950 |