Mining alternative actions from community Q&A corpus for task-oriented web search
Mining alternative actions from community Q&A corpus for task-oriented web search
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DOI：
10.1145/3106426.3106461
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发表时间：
2017-08
期刊：
影响因子：
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通讯作者：
Suppanut Pothirattanachaikul;Takehiro Yamamoto;Sumio Fujita;Akira Tajima;Katsumi Tanaka
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文献类型：
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作者：
Suppanut Pothirattanachaikul;Takehiro Yamamoto;Sumio Fujita;Akira Tajima;Katsumi Tanaka
网络搜索者经常使用网络搜索引擎来找到实现他/她目标的方法或手段。例如，一个用户想要解决他/她的睡眠问题，查询“安眠药”可能会被使用。然而，也许还有另一种方法可以达到同样的目的，比如“喝杯热牛奶”或“睡前散步”。问题是用户可能不知道这些解决方案的存在。因此，他/她可能会选择服用安眠药而不考虑这些解决方案。在本研究中，我们定义并解决了备选动作挖掘问题。特别是，我们试图开发一种方法来挖掘给定查询的替代操作。我们将备选行动定义为具有相同目标的行动，并将备选行动挖掘问题定义为搜索结果多样化相似的行动。为了解决这个问题，我们建议利用社区问答（cQA）语料库来挖掘替代行动。我们提出了一种方法，通过使用cQA语料库中的问答结构来计算两个动作如何很好地替代动作。我们的方法建立了一个问题-行动二部图，并递归地计算两个行动作为备选行动的程度。我们使用两个新构建的测试集合（每个集合包含50个查询）进行了实验，以调查我们的方法的有效性。实验结果表明，该方法在d# -nDCG方面优于商业搜索引擎提供的查询建议方法。
Web searchers often use a Web search engine to find a way or means to achieve his/her goal. For example, a user intending to solve his/her sleeping problem, the query "sleeping pills" may be used. However, there may be another solution to achieve the same goal, such as "have a cup of hot milk" or "stroll before bedtime." The problem is that the user may not be aware that these solutions exist. Thus, he/she will probably choose to take a sleeping pill without considering these solutions. In this study, we define and tackle the alternative action mining problem. In particular, we attempt to develop a method for mining alternative actions for a given query. We define alternative actions as actions which share the same goal and define the alternative action mining problem as similar in the search result diversification. To tackle the problem, we propose leveraging a community Q&A (cQA) corpus for mining alternative actions. We propose a method to compute how well two actions can be alternative actions by using a question-answer structure in a cQA corpus. Our method builds a question-action bipartite graph and recursively computes how well two actions can be alternative actions. We conducted experiments to investigate the effectiveness of our method using two newly built test collections, each containing 50 queries. The experimental results indicated that our proposed method outperformed the query suggestion methods provided by the commercial search engines in terms of D#-nDCG.