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

Wongchokprasitti, Chirayu; Peltonen, Jaakko; Ruotsalo, Tuukka; Bandyopadhyay, Payel; Brusilovsky, Peter

doi:10.1007/978-3-319-20267-9_24

Cited by 17 publications

(9 citation statements)

References 7 publications

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“…Interactive interfaces that enable transparent control on user models have recently become popular [35,36,2,4,42,32]. The idea behind these approaches is that, as opposite to visualizing results, the user model is visualized and the user can interactively provide feedback on the search intentions using the visualization.…”

Section: Search Results Visualizationmentioning

confidence: 99%

Topic-Relevance Map

Peltonen

Belorustceva

Ruotsalo

2017

Proceedings of the 22nd International Conference on Intelligent User Interfaces

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We introduce topic-relevance map, an interactive search result visualization that assists rapid information comprehension across a large ranked set of results. The topic-relevance map visualizes a topical overview of the search result space as keywords with respect to two essential information retrieval measures: relevance and topical similarity. Non-linear dimensionality reduction is used to embed high-dimensional keyword representations of search result data into angles on a radial layout. Relevance of keywords is estimated by a ranking method and visualized as radiuses on the radial layout. As a result, similar keywords are modeled by nearby points, dissimilar keywords are modeled by distant points, more relevant keywords are closer to the center of the radial display, and less relevant keywords are distant from the center of the radial display. We evaluated the effect of the topic-relevance map in a search result comprehension task where 24 participants were summarizing search results and produced a conceptualization of the result space. The results show that topic-relevance map significantly improves participants' comprehension capability compared to a conventional ranked list presentation.

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Section: Search Results Visualizationmentioning

confidence: 99%

Topic-Relevance Map

Peltonen

Belorustceva

Ruotsalo

2017

Proceedings of the 22nd International Conference on Intelligent User Interfaces

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“…Wongchokprasitti et al [23] propose using a user model that can be shared amongst recommendation services, with a system that maintains the user models based on user interactions with each of these services. If a single service that provides recommendations in multiple domains fits this description, this is most similar to our proposed approach.…”

Section: Cross-domain Recommendationsmentioning

confidence: 99%

Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste

Nazari

Charbuillet

Pages

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Recommender systems are increasingly used to predict and serve content that aligns with user taste, yet the task of matching new users with relevant content remains a challenge. We consider podcasting to be an emerging medium with rapid growth in adoption, and discuss challenges that arise when applying traditional recommendation approaches to address the cold-start problem. Using music consumption behavior, we examine two main techniques in inferring Spotify users preferences over more than 200k podcasts. Our results show significant improvements in consumption of up to 50% for both offline and online experiments. We provide extensive analysis on model performance and examine the degree to which music data as an input source introduces bias in recommendations.

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“…Support for exploratory search has involved term or query suggestions [18], facets [14] and (cluster-based) result visualization [12], time-consuming feedback mechanisms, or further focus within the initial scope [14]. We instead concentrate on a recent novel framework for interactive information retrieval with intent modeling, which uses a visualization to display estimates of the user intent and allows the user to adjust them through manipulation of keywords ( [25]; see also, e.g., [1,23,31] for further developments of the framework).…”

Section: Iui 2017 • Information Visualisationmentioning

confidence: 99%

Negative Relevance Feedback for Exploratory Search with Visual Interactive Intent Modeling

Peltonen

Strahl

Floréen

2017

Proceedings of the 22nd International Conference on Intelligent User Interfaces

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In difficult information seeking tasks, the majority of topranked documents for an initial query may be non-relevant, and negative relevance feedback may then help find relevant documents. Traditional negative relevance feedback has been studied on document results; we introduce a system and interface for negative feedback in a novel exploratory search setting, where continuous-valued feedback is directly given to keyword features of an inferred probabilistic user intent model. The introduced system allows both positive and negative feedback directly on an interactive visual interface, by letting the user manipulate keywords on an optimized visualization of modeled user intent. Feedback on the interactive intent model lets the user direct the search: Relevance of keywords is estimated from feedback by Bayesian inference, influence of feedback is increased by a novel propagation step, documents are retrieved by likelihoods of relevant versus non-relevant intents, and the most relevant keywords (having the highest upper confidence bounds of relevance) and the most non-relevant ones (having the smallest lower confidence bounds of relevance) are shown as options for further feedback. We carry out task-based information seeking experiments with real users on difficult real tasks; we compare the system to the nearest state of the art baseline allowing positive feedback only, and show negative feedback significantly improves the quality of retrieved information and user satisfaction for difficult tasks.

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

Cited by 17 publications

References 7 publications

Topic-Relevance Map

Topic-Relevance Map

Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste

Negative Relevance Feedback for Exploratory Search with Visual Interactive Intent Modeling

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