1,695 research outputs found
Intent Models for Contextualising and Diversifying Query Suggestions
The query suggestion or auto-completion mechanisms help users to type less
while interacting with a search engine. A basic approach that ranks suggestions
according to their frequency in the query logs is suboptimal. Firstly, many
candidate queries with the same prefix can be removed as redundant. Secondly,
the suggestions can also be personalised based on the user's context. These two
directions to improve the aforementioned mechanisms' quality can be in
opposition: while the latter aims to promote suggestions that address search
intents that a user is likely to have, the former aims to diversify the
suggestions to cover as many intents as possible. We introduce a
contextualisation framework that utilises a short-term context using the user's
behaviour within the current search session, such as the previous query, the
documents examined, and the candidate query suggestions that the user has
discarded. This short-term context is used to contextualise and diversify the
ranking of query suggestions, by modelling the user's information need as a
mixture of intent-specific user models. The evaluation is performed offline on
a set of approximately 1.0M test user sessions. Our results suggest that the
proposed approach significantly improves query suggestions compared to the
baseline approach.Comment: A short version of this paper was presented at CIKM 201
Theory-based user modeling for personalized interactive information retrieval
In an effort to improve users’ search experiences during their information seeking process, providing a personalized information retrieval system is proposed to be one of the effective approaches. To personalize the search systems requires a good understanding of the users. User modeling has been approved to be a good method for learning and representing users. Therefore many user modeling studies have been carried out and some user models have been developed. The majority of the user modeling studies applies inductive approach, and only small number of studies employs deductive approach. In this paper, an EISE (Extended Information goal, Search strategy and Evaluation threshold) user model is proposed, which uses the deductive approach based on psychology theories and an existing user model. Ten users’ interactive search log obtained from the real search engine is applied to validate the proposed user model. The preliminary validation results show that the EISE model can be applied to identify different types of users. The search preferences of the different user types can be applied to inform interactive search system design and development
Efficient Diversification of Web Search Results
In this paper we analyze the efficiency of various search results
diversification methods. While efficacy of diversification approaches has been
deeply investigated in the past, response time and scalability issues have been
rarely addressed. A unified framework for studying performance and feasibility
of result diversification solutions is thus proposed. First we define a new
methodology for detecting when, and how, query results need to be diversified.
To this purpose, we rely on the concept of "query refinement" to estimate the
probability of a query to be ambiguous. Then, relying on this novel ambiguity
detection method, we deploy and compare on a standard test set, three different
diversification methods: IASelect, xQuAD, and OptSelect. While the first two
are recent state-of-the-art proposals, the latter is an original algorithm
introduced in this paper. We evaluate both the efficiency and the effectiveness
of our approach against its competitors by using the standard TREC Web
diversification track testbed. Results shown that OptSelect is able to run two
orders of magnitude faster than the two other state-of-the-art approaches and
to obtain comparable figures in diversification effectiveness.Comment: VLDB201
查询推荐研究综述
查询推荐是一种提高用户搜索效率的重要技术,其核心任务是帮助用户构造有效查询并以此准确描述用户信息需求。作为当今搜索引擎的核心技术之一,查询推荐吸引了学术界和工业界的广泛关注,一直以来都是信息检索领域中重要的研究主题。本文以国内外会议、期刊发表的有关查询推荐研究的文献为对象,利用归纳总结方法,首先详细梳理了查询推荐中主流方法——基于简单共现信息的方法、基于图模型的方法以及融合多种信息的方法,然后总结评述了评测方法与指标,最后分析了未来可能的研究方向。</p
Interactive Intent Modeling for Exploratory Search
Exploratory search requires the system to assist the user in comprehending the information space and expressing evolving search intents for iterative exploration and retrieval of information. We introduce interactive intent modeling, a technique that models a user’s evolving search intents and visualizes them as keywords for interaction. The user can provide feedback on the keywords, from which the system learns and visualizes an improved intent estimate and retrieves information. We report experiments comparing variants of a system implementing interactive intent modeling to a control system. Data comprising search logs, interaction logs, essay answers, and questionnaires indicate significant improvements in task performance, information retrieval performance over the session, information comprehension performance, and user experience. The improvements in retrieval effectiveness can be attributed to the intent modeling and the effect on users’ task performance, breadth of information comprehension, and user experience are shown to be dependent on a richer visualization. Our results demonstrate the utility of combining interactive modeling of search intentions with interactive visualization of the models that can benefit both directing the exploratory search process and making sense of the information space. Our findings can help design personalized systems that support exploratory information seeking and discovery of novel information.Peer reviewe
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