Query suggestion with diversification and personalization

Jiang, Di; Leung, Kenneth Wai-Ting; Yang, Lingxiao; Ng, Wilfred

doi:10.1016/j.knosys.2015.09.003

Cited by 12 publications

(5 citation statements)

References 34 publications

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“…Moreover, research on query suggestion is relevant to our work if we consider suggesting queries as a means of clarifying users' intent in a traditional IR setting [33]. Result diversification and personalizing is one of the key components for query suggestion [20], especially when applied to small-screen devices. In particular, Kato and Tanaka [21] found that presenting results for one facet and suggesting queries for other facets is more effective on such devices.…”

Section: Related Workmentioning

confidence: 99%

Asking Clarifying Questions in Open-Domain Information-Seeking Conversations

Aliannejadi

Zamani

Crestani

et al. 2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

221

290

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Users often fail to formulate their complex information needs in a single query. As a consequence, they may need to scan multiple result pages or reformulate their queries, which may be a frustrating experience. Alternatively, systems can improve user satisfaction by proactively asking questions of the users to clarify their information needs. Asking clarifying questions is especially important in conversational systems since they can only return a limited number of (often only one) result(s).In this paper, we formulate the task of asking clarifying questions in open-domain information-seeking conversational systems. To this end, we propose an offline evaluation methodology for the task and collect a dataset, called Qulac, through crowdsourcing. Our dataset is built on top of the TREC Web Track 2009-2012 data and consists of over 10K question-answer pairs for 198 TREC topics with 762 facets. Our experiments on an oracle model demonstrate that asking only one good question leads to over 170% retrieval performance improvement in terms of P@1, which clearly demonstrates the potential impact of the task. We further propose a retrieval framework consisting of three components: question retrieval, question selection, and document retrieval. In particular, our question selection model takes into account the original query and previous question-answer interactions while selecting the next question. Our model significantly outperforms competitive baselines. To foster research in this area, we have made Qulac publicly available.

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Section: Related Workmentioning

confidence: 99%

Asking Clarifying Questions in Open-Domain Information-Seeking Conversations

Aliannejadi

Zamani

Crestani

et al. 2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

221

290

View full text Add to dashboard Cite

show abstract

“…Information retrieval community has paid close attention to the problem of ambiguity in user search queries. Previously this problem was addressed through the diversification of search result pages (Radlinski and Dumais, 2006;Allan, 2016, 2014), including via usage of personal and contextual data (Jiang et al, 2015;Kato and Tanaka, 2016). Recently, (Rosset et al, 2020;Aliannejadi et al, 2019;Zamani et al, 2020a) suggest techniques to address ambiguity by generating clarifying questions.…”

Section: Learning To Ask Clarifying Questionsmentioning

confidence: 99%

Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions

Aliannejadi¹,

Kiseleva²,

Chuklin³

et al. 2021

Preprint

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Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response. Namely, for cases when a user request is not specific enough for a conversation system to provide an answer right away, it is desirable to ask a clarifying question to increase the chances of retrieving a satisfying answer. To address the problem of 'asking clarifying questions in opendomain dialogues': (1) we collect and release a new dataset focused on open-domain singleand multi-turn conversations, (2) we benchmark several state-of-the-art neural baselines, and (3) we propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues. These contributions are suitable as a foundation for further research.

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“…Some researchers have tried to develop different keyword suggestion systems to provide relevant recommendations to users. Jiang et al [72] proposed a new keyword suggestion paradigm to effectively integrate diversity and personalization into a unified framework. In this framework, the suggested keywords are effectively diversified to cover different aspects of the input query while personalizing the ranking of suggested keywords to ensure that the highest ranked keywords are consistent with the user's personal preferences.…”

Section: Recommendation Systemmentioning

confidence: 99%

A Study of Optimizing Search Engine Results Through User Interaction

Chen

2020

IEEE Access

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In this paper, we propose a method for optimizing search engine results based on user interaction. This method generates different search results mainly through the user's operation on different search topics. There are two main differences between our method and the traditional personalized search method: personal privacy and storage space. First, traditional personal search methods need to record search and click records for individual users. However, these records have great personal privacy issues for users; especially in recent years, there have been personal privacy leaks that occurred in many large online companies (such as Facebook and Yahoo). Secondly, because different users need to record their own search records, the size of the storage space is closely related to the number of users and the amount of search records stored. However, the sum of individual users' search and click records is a huge storage space in today's Internet environment. To avoid these two issues for traditional personalized search methods, this study proposes a storage-free approach based on individual users operating on different search topics. In general, in addition to avoiding personal privacy and storage space issues, our method can also achieve optimal linear time in generating personalized search results. INDEX TERMS Document clustering, machine learning, natural language processing, personalized topic search, user log.

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Query suggestion with diversification and personalization

Cited by 12 publications

References 34 publications

Asking Clarifying Questions in Open-Domain Information-Seeking Conversations

Asking Clarifying Questions in Open-Domain Information-Seeking Conversations

Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions

A Study of Optimizing Search Engine Results Through User Interaction

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