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A comparison of unimodal and multimodal models for implicit detection of relevance in interactive IR
Author(s) -
GonzálezIbáñez Roberto,
EsparzaVillamán Aileen,
VargasGodoy Juan Carlos,
Shah Chirag
Publication year - 2019
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.24202
Subject(s) - unimodality , relevance (law) , multimodality , univariate , computer science , artificial intelligence , machine learning , feature (linguistics) , information retrieval , natural language processing , linguistics , mathematics , multivariate statistics , statistics , political science , law , philosophy , world wide web
Implicit detection of relevance has been approached by many during the last decade. From the use of individual measures to the use of multiple features from different sources (multimodality), studies have shown the feasibility to automatically detect whether a document is relevant. Despite promising results, it is not clear yet to what extent multimodality constitutes an effective approach compared to unimodality. In this article, we hypothesize that it is possible to build unimodal models capable of outperforming multimodal models in the detection of perceived relevance. To test this hypothesis, we conducted three experiments to compare unimodal and multimodal classification models built using a combination of 24 features. Our classification experiments showed that a univariate unimodal model based on the left‐click feature supports our hypothesis. On the other hand, our prediction experiment suggests that multimodality slightly improves early classification compared to the best unimodal models. Based on our results, we argue that the feasibility for practical applications of state‐of‐the‐art multimodal approaches may be strongly constrained by technology, cultural, ethical, and legal aspects, in which case unimodality may offer a better alternative today for supporting relevance detection in interactive information retrieval systems.