Studies on User Intent Analysis and Mining

2021 ◽  
Author(s):  
Yue Shang
Keyword(s):  
User Intent

Modeling Instant User Intent and Content-level Transition for Sequential Fashion Recommendation

2021 ◽  
pp. 1-1
Author(s):  
Yujuan Ding ◽  
Yunshan Ma ◽  
Waikeung Wong ◽  
Tat-Seng Chua
Keyword(s):  
User Intent ◽  
Level Transition

You are where you go, the commodification of daily life through ‘location’

2017 ◽  
Vol 49 (12) ◽  
pp. 2702-2717 ◽  
Author(s):  
Jim Thatcher
Keyword(s):  
Data Mining ◽  
Everyday Life ◽  
Mobile Application ◽  
Daily Life ◽  
Digital Data ◽  
Late Capitalism ◽  
User Intent ◽  
Data Object ◽  
Other Information ◽  
Sensor Information

Recent years have seen an explosion in the investment into and valuation of mobile spatial applications. With multiple applications currently valued at well over one billion U.S. dollars, mobile spatial applications and the data they generate have come to play an increasingly significant role in the function of late capitalism. Empirically based upon a series of interviews conducted with mobile application designers and developers, this article details the creation of a digital commodity termed ‘location.’ ‘Location’ is developed through three discursive poles: Its storing of space and time as digital data object manipulable by code, its spatial and temporal immediacy, and its ability to ‘add value’ or ‘tell a story’ to both end-users and marketers. As a commodity it represents the sum total of targeted marking information, including credit profiles, purchase history, and a host of other information available through data mining or sensor information, combined with temporal immediacy, physical location, and user intent. ‘Location’ is demonstrated to exist as a commodity from its very inception and, as such, to be a key means through which everyday life is further entangled with processes of capitalist exploitation.

EdgeDIPN

2020 ◽  
Vol 14 (3) ◽  
pp. 320-328
Author(s):  
Long Guo ◽  
Lifeng Hua ◽  
Rongfei Jia ◽  
Fei Fang ◽  
Binqiang Zhao ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Attention Mechanism ◽  
Data Sources ◽  
User Intent ◽  
Multiple User ◽  
Shopping Experience ◽  
Data Source ◽  
Intent Prediction ◽  
The Right

With the rapid growth of e-commerce in recent years, e-commerce platforms are becoming a primary place for people to find, compare and ultimately purchase products. To improve online shopping experience for consumers and increase sales for sellers, it is important to understand user intent accurately and be notified of its change timely. In this way, the right information could be offered to the right person at the right time. To achieve this goal, we propose a unified deep intent prediction network, named EdgeDIPN, which is deployed at the edge, i.e., mobile device, and able to monitor multiple user intent with different granularity simultaneously in real-time. We propose to train EdgeDIPN with multi-task learning, by which EdgeDIPN can share representations between different tasks for better performance and saving edge resources in the meantime. In particular, we propose a novel task-specific attention mechanism which enables different tasks to pick out the most relevant features from different data sources. To extract the shared representations more effectively, we utilize two kinds of attention mechanisms, where the multi-level attention mechanism tries to identify the important actions within each data source and the inter-view attention mechanism learns the interactions between different data sources. In the experiments conducted on a large-scale industrial dataset, EdgeDIPN significantly outperforms the baseline solutions. Moreover, EdgeDIPN has been deployed in the operational system of Alibaba. Online A/B testing results in several business scenarios reveal the potential of monitoring user intent in real-time. To the best of our knowledge, EdgeDIPN is the first full-fledged real-time user intent understanding center deployed at the edge and serving hundreds of millions of users in a large-scale e-commerce platform.

scholarly journals Capturing User Intent when Brushing In Scatterplots

2020 ◽  
Author(s):  
Kiran Gadhave ◽  
Jochen Görtler ◽  
Oliver Deussen ◽  
Miriah Meyer ◽  
Jeff Phillips ◽  
...  
Keyword(s):  
Visualization Tool ◽  
Domain Experts ◽  
User Intent ◽  
Selection Accuracy ◽  
Provenance Data ◽  
Potential Benefits ◽  
Concise Representation ◽  
Mixed Initiative ◽  
Selection Of

Being able to capture or predict a user's intent behind a brush in a visualization tool has important implications in two scenarios. First, predicting intents can be used to auto-complete a partial selection in a mixed-initiative approach, with potential benefits to selection speed, correctness, and confidence. Second, capturing the intent of a selection can be used to improve recall, reproducibility, and even re-use. Augmenting provenance logs with semi-automatically captured intents makes it possible to save the reasoning behind selections. In this paper, we introduce a method to infer intent for selections and brushes in scatterplots. We first introduce a taxonomy of types of patterns that users might specify, which we elicited in a formative study conducted with professional data analysts and scientists. Based on this, we identify algorithms that can classify these patterns, and introduce various approaches to score the match of each pattern to an analyst's selection of items. We introduce a system that implements these methods for scatterplots and ranks alternative patterns against each other. Analysts then can use these predictions to auto-complete partial selections, and to conveniently capture their intent and provide annotations, thus making a concise representation of that intent available to be stored as provenance data. We evaluate our approach using interviews with domain experts and in a quantitative crowd-sourced study, in which we show that using auto-complete leads to improved selection accuracy for most types of patterns.

Context and Community Awareness in Support of User Intent Prediction

2014 ◽  
pp. 359-378 ◽  
Author(s):  
Nikos Kalatzis ◽  
Ioanna Roussaki ◽  
Nicolas Liampotis ◽  
Pavlos Kosmides ◽  
Ioannis Papaioannou ◽  
...  
Keyword(s):  
User Intent ◽  
Community Awareness ◽  
Intent Prediction
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