| 其他摘要 | With the development of information technology, today's intelligent assistants can provide proactive activity recommendations for what users could do in the upcoming moments, thereby improving their interaction efficiency. However, activity recommendation, as an emerging technology, still suffers from imperfect recommendation logic and low user acceptance, and there is also a lack of targeted research. The main purpose of this study is to construct a predictive model for the acceptance of activity recommendations from intelligent systems. Given the multi-task management decision essence of the acceptance process, daily cell phone activity recommendations and in-car activity recommendations were selected as typical cases in task-switching and parallel processing contexts, respectively. How the characteristics of the user's ongoing task, those of the task recommended by the system, and other factors such as individual characteristics jointly affect the user's acceptance of the recommendation were examined.
Study la first measured the distribution of common activity recommendation scenarios and tasks on key features based on a questionnaire survey (N=220) and selected representative daily cell phone tasks for the follow-up experiment. The hierarchical linear regression model for task evaluation indicated that users' perceived load and involvement of the tasks positively influenced their stickiness in them, and thus might impose restrictions on activity recommendations. Study 1b experimented on daily cell phone activity recommendation acceptance in a task-switching context (N=38), adopting an experimental paradigm that combined task experience and evaluation with simulated switching decisions. Recommendation acceptance was modeled with in-task ECG and EDA physiological indicators incorporated. The hierarchical logistic regression model with subjects at the second level showed that the involvement and priority of the current task negatively predicted the acceptance of the recommended task; the load of the recommended task negatively predicted its acceptance, while its involvement and priority positively predicted it. Physiological indicators such as inter-beat interval and skin conductance response also had additional predictive power for recommendation acceptance. The total explanatory power of the model amounted to 35%.
Study 2 further experimented on activity recommendation acceptance in a parallel processing context using an autonomous driving monitoring scenario (N=47), adopting a similar experimental paradigm that combined experience and evaluation of the recommended tasks with simulated responses to recommendations during driving. A takeover task with cognitive judgment requirements was added to the experiment. The acceptance of recommendations and subsequent takeover performance were modeled respectively. The hierarchical logistic regression model for decisions to accept showed that the involvement of the driving task positively predicted the acceptance of the recommended task, while its priority negatively predicted recommendation acceptance; the involvement, priority, and interruptibility of the recommendation task positively predicted its acceptance, while the required completion time and resource competition with the driving task negatively predicted acceptance. In addition, subjects who believed that they could better identify the impact of distraction on driving and regulate their distraction based on driving conditions were more likely to accept recommendations. The total explanatory power of the model was 36%. In terms of safety consequences, the takeover success rate was lower and the reaction time was longer under a higher level of driving task load. Compared to the baseline condition where no recommendation was provided, subjects' takeover performance was impaired after accepting a recommendation task, while their reaction time was shortened after rejecting a recommendation, reflecting the differences in effects recommendation responses.
In general, the influence of the main task characteristics on the recommendation acceptance decision has certain commonality in the two scenarios. The recommended task is always more likely to be accepted when the priority of the current task is lower, the load of the recommended task is lower and its involvement and priority are higher. The influence of complementary factors in the parallel processing context further demonstrates the importance of inter-task compatibility. The current study extends the existing findings on multi-task management and driving distraction in the emerging field of activity recommendation. It also provides a rationale for the prediction and improvement of activity recommendation results, and the constraints on their impact. Future research may proceed to explore variants of the model in different application scenarios and task-processing contexts to further promote activity recommendation and enhance its experience. |
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