Directing exploratory search with interactive intent modeling

Ruotsalo, Tuukka; Peltonen, Jaakko; Eugster, Manuel J. A.; Głowacka, Dorota; Konyushkova, Ksenia; Athukorala, Kumaripaba; Kosunen, Ilkka; Reijonen, Aki; Myllymäki, Petri; Jacucci, Giulio; Kaski, Samuel

doi:10.1145/2505515.2505644

Cited by 68 publications

(68 citation statements)

References 15 publications

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“…In a practical information retrieval application that can benefit from relevance prediction, the target is to detect true positive examples of terms that represent user's search intent [34]. In such applications, a classifier that trades recall for the benefit of precision can be used to maximize user experience.…”

Section: High-precision Trpbmentioning

confidence: 99%

Predicting term-relevance from brain signals

Eugster

Ruotsalo

Spapé

et al. 2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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Term-Relevance Prediction from Brain Signals (TRPB) is proposed to automatically detect relevance of text information directly from brain signals. An experiment with forty participants was conducted to record neural activity of participants while providing relevance judgments to text stimuli for a given topic. High-precision scientific equipment was used to quantify neural activity across 32 electroencephalography (EEG) channels. A classifier based on a multi-view EEG feature representation showed improvement up to 17% in relevance prediction based on brain signals alone. Relevance was also associated with brain activity with significant changes in certain brain areas. Consequently, TRPB is based on changes identified in specific brain areas and does not require user-specific training or calibration. Hence, relevance predictions can be conducted for unseen content and unseen participants. As an application of TRPB we demonstrate a high-precision variant of the classifier that constructs sets of relevant terms for a given unknown topic of interest. Our research shows that detecting relevance from brain signals is possible and allows the acquisition of relevance judgments without a need to observe any other user interaction. This suggests that TRPB could be used in combination or as an alternative for conventional implicit feedback signals, such as dwell time or click-through activity.

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Section: High-precision Trpbmentioning

confidence: 99%

Predicting term-relevance from brain signals

Eugster

Ruotsalo

Spapé

et al. 2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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“…SciNet [7] is an exploratory search system. SciNet indexes over 50 million scientific documents from Thomson Reuters, ACM, IEEE, and Springer.…”

Section: Setting: Two Academic Information Systemsmentioning

confidence: 99%

“…Going beyond text-based queries, SciNet helps users direct exploratory search by allowing them to interact with an open user model discussed below. The approach (called "interactive intent modeling" in [7]) significantly improved users? information seeking task performance and quality of retrieved information [7], thus the open user models are promising for cross-system transfer.…”

Section: Setting: Two Academic Information Systemsmentioning

confidence: 99%

User Model in a Box: Cross-System User Model Transfer for Resolving Cold Start Problems

Wongchokprasitti

Peltonen

Ruotsalo

et al. 2015

Lecture Notes in Computer Science

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Abstract.Recommender systems face difficulty in cold-start scenarios where a new user has provided only few ratings. Improving cold-start performance is of great interest. At the same time, the growing number of adaptive systems makes it ever more likely that a new user in one system has already been a user in another system in related domains. To what extent can a user model built by one adaptive system help address a cold start problem in another system? We compare methods of cross-system user model transfer across two large real-life systems: we transfer user models built for information seeking of scientific articles in the SciNet exploratory search system, operating over tens of millions of articles, to perform cold-start recommendation of scientific talks in the CoMeT talk management system, operating over hundreds of talks. Our user study focuses on transfer of novel explicit open user models curated by the user during information seeking. Results show strong improvement in cold-start talk recommendation by transferring open user models, and also reveal why explicit open models work better in cross-domain context than traditional hidden implicit models.

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“…Quite an amount of work leverage query logs (Jiang et al, 2013), including query reformulations (Radlinski et al, 2010), click-through data (Li et al, 2008). There are also works using sponsered data (Yamamoto et al, 2012) and interactive data (Ruotsalo et al, 2013). The new trend of integrating knowledge graph will be discussed next.…”

Section: Query Intent Understandingmentioning

confidence: 99%

Tailor knowledge graph for query understanding: linking intent topics by propagation

Zhao

Zhang

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Knowledge graphs are recently used for enriching query representations in an entity-aware way for the rich facts organized around entities in it.However, few of the methods pay attention to non-entity words and clicked websites in queries, which also help conveying user intent. In this paper, we tackle the problem of intent understanding with innovatively representing entity words, refiners and clicked urls as intent topics in a unified knowledge graph based framework, in a way to exploit and expand knowledge graph which we call 'tailor'. We collaboratively exploit global knowledge in knowledge graphs and local contexts in query log to initialize intent representation, then propagate the enriched features in a graph consisting of intent topics using an unsupervised algorithm. The experiments prove intent topics with knowledge graph enriched features significantly enhance intent understanding.

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Directing exploratory search with interactive intent modeling

Cited by 68 publications

References 15 publications

Predicting term-relevance from brain signals

Predicting term-relevance from brain signals

User Model in a Box: Cross-System User Model Transfer for Resolving Cold Start Problems

Tailor knowledge graph for query understanding: linking intent topics by propagation

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