论文标题
您想听听新闻吗？调查基于语音的对话新闻建议
Would You Like to Hear the News? Investigating Voice-BasedSuggestions for Conversational News Recommendation
论文作者
论文摘要
基于语音的个人助理的关键好处之一是有可能主动推荐相关和有趣的信息。此类信息最有价值的来源之一是新闻。但是，为了使用户听到有用的新闻和与之相关的新闻，必须以有趣且有益的方式推荐。但是，据我们所知，如何为基于语音的推荐提供新闻项目仍然是一个悬而未决的问题。在本文中，我们从经验上比较了基于语音的对话环境中推荐新闻或特定新闻项目的不同方式。具体来说，我们研究了用户参与度和满意度，其中五种不同的新闻建议：（1）通用新闻简报； （2）有关与当前对话有关的特定实体的新闻； （3）关于过去对话的实体的新闻； （4）有关热门新闻主题的新闻； （5）默认值 - 一般谈论新闻的建议。我们的结果表明，与简报建议相比，基于实体的新闻建议的接受度比建议更高29％，与推荐通用或趋势新闻相比，接受率几乎高100％。我们对新闻建议的介绍以及由此产生的见解的调查可能会使语音助手更有信息和吸引力。
One of the key benefits of voice-based personal assistants is the potential to proactively recommend relevant and interesting information. One of the most valuable sources of such information is the News. However, in order for the user to hear the news that is useful and relevant to them, it must be recommended in an interesting and informative way. However, to the best of our knowledge, how to present a news item for a voice-based recommendation remains an open question. In this paper, we empirically compare different ways of recommending news, or specific news items, in a voice-based conversational setting. Specifically, we study the user engagement and satisfaction with five different variants of presenting news recommendations: (1) a generic news briefing; (2) news about a specific entity relevant to the current conversation; (3) news about an entity from a past conversation; (4) news on a trending news topic; and (5) the default - a suggestion to talk about news in general. Our results show that entity-based news recommendations exhibit 29% higher acceptance compared to briefing recommendations, and almost 100% higher acceptance compared to recommending generic or trending news. Our investigation into the presentation of news recommendations and the resulting insights could make voice assistants more informative and engaging.
