Say Your Reason: Extract Contextual Rules In Situ for Context-aware Service Recommendation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917758780833792 |
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| author | Li, Yuxuan Li, Jiahui Pan, Lihang Yu, Chun Shi, Yuanchun |
| author_facet | Li, Yuxuan Li, Jiahui Pan, Lihang Yu, Chun Shi, Yuanchun |
| contents | This paper introduces SayRea, an interactive system that facilitates the extraction of contextual rules for personalized context-aware service recommendations in mobile scenarios. The system monitors a user's execution of registered services on their smartphones (via accessibility service) and proactively requests a single-sentence reason from the user. By utilizing a Large Language Model (LLM), SayRea parses the reason and predicts contextual relationships between the observed service and potential contexts (such as setting the alarm clock deep in the evening). In this way, SayRea can significantly reduce the cognitive load on users in anticipating future needs and selecting contextual attributes. A 10-day field study involving 20 participants showed that SayRea accumulated an average of 62.4 rules per user and successfully recommended 45% of service usage. The participants provided positive feedback on the system's usability, interpretability, and controllability. The findings highlight SayRea's effectiveness in personalized service recommendations and its potential to enhance user experience in mobile scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13977 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Say Your Reason: Extract Contextual Rules In Situ for Context-aware Service Recommendation Li, Yuxuan Li, Jiahui Pan, Lihang Yu, Chun Shi, Yuanchun Human-Computer Interaction This paper introduces SayRea, an interactive system that facilitates the extraction of contextual rules for personalized context-aware service recommendations in mobile scenarios. The system monitors a user's execution of registered services on their smartphones (via accessibility service) and proactively requests a single-sentence reason from the user. By utilizing a Large Language Model (LLM), SayRea parses the reason and predicts contextual relationships between the observed service and potential contexts (such as setting the alarm clock deep in the evening). In this way, SayRea can significantly reduce the cognitive load on users in anticipating future needs and selecting contextual attributes. A 10-day field study involving 20 participants showed that SayRea accumulated an average of 62.4 rules per user and successfully recommended 45% of service usage. The participants provided positive feedback on the system's usability, interpretability, and controllability. The findings highlight SayRea's effectiveness in personalized service recommendations and its potential to enhance user experience in mobile scenarios. |
| title | Say Your Reason: Extract Contextual Rules In Situ for Context-aware Service Recommendation |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2408.13977 |