Intelligent context based prediction using probabilistic intent-action ontology and tone matching algorithm

Kulkarni, Hrishikesh

doi:10.1109/icacci.2017.8125916

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…After dividing users by purchase rate, Kaneko and Yada [20] constructed an online prediction model for user purchase rate based on beta geometric/negative binomial distribution (BG/NBD), which can accurately forecast user purchase behaviors in 2.5 years. To predict the purchase behaviors of Taobao users, Kulkarni [21] added three concomitant variables, namely, the number of reviews, the number of favorites, and the repeat purchase rate, to the hybrid model of SVM and HIPP. Robinson et al [22] introduced the recency-frequency-monetary (RFM) model into the association rules of the traditional BG/NBD model, which, coupled with the update of weight coefficients, can process online purchase information, and predict user purchase trend in real time.…”

Section: Introductionmentioning

confidence: 99%

Design and Application of a Prediction Model for User Purchase Intention Based on Big Data Analysis

Zhang¹

2020

ISI

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With the proliferation of information technology, precision marketing has emerged as an important strategy to increase the return rate. To improve the effect of precision marketing, the traditional manual survey should be replaced with big data analysis to disclose user demand and understand user purchase behaviors. For accurate forecast of user purchase behaviors, this paper firstly analyzes the quality and features of the historical data on user purchases, and preprocesses the data by a self-designed procedure. Then, the various features of user purchase behaviors were summarized and optimized, and the features of user purchase intentions for products were extracted. On this basis, a DenseNet purchase intention prediction model was established on Bagging strategy. The effectiveness of the proposed model was proved through experiments. Our model enables enterprises to quickly identify potential sales targets.

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Section: Introductionmentioning

confidence: 99%

Design and Application of a Prediction Model for User Purchase Intention Based on Big Data Analysis

Zhang¹

2020

ISI

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“…Researchers worked on deriving emotions based on expressions of individuals. This expressions are considered in specific scenario to understand context [11]. This paper focuses on embedding emotions in advertisement and news.…”

Section: Introductionmentioning

confidence: 99%

Computational Psychology to Embed Emotions into Product to Increase Customer Affinity

Kulkarni

Joshi²,

Chande³

2019

ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management

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Readers take decisions about going through the complete news based on many factors. The emotional impact of the news title on reader is one of the most important factors. Cognitive ergonomics tries to strike the balance between work, product and environment with human needs and capabilities. The utmost need to integrate emotions in the news as well as advertisements cannot be denied. The idea is that news or advertisement should be able to engage the reader on emotional and behavioral platform. While achieving this objective there is need to learn about reader behavior and use computational psychology while presenting as well as writing news or advertisements. This paper based on Machine Learning, tries to map behavior of the reader with the news/advertisements and also provide inputs for affective value for building personalized news or advertisements presentations. The affective value of the news is determined and news artifacts are mapped to reader. The algorithm suggests the most suitable news for readers while understanding emotional traits required for personalization. This work can be used to improve reader satisfaction through embedding emotions in the reading material and prioritizing news presentations. It can be used to map personal reading material range, personalized programs and ranking programs, advertisements with reference to individuals.

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Multi-graph-Based Intent Hierarchy Generation to Determine Action Sequence

Kulkarni

2018

Proceedings of the 2nd International Conference on Data Engineering and Communication Technology

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Intelligent context based prediction using probabilistic intent-action ontology and tone matching algorithm

Cited by 6 publications

References 8 publications

Design and Application of a Prediction Model for User Purchase Intention Based on Big Data Analysis

Design and Application of a Prediction Model for User Purchase Intention Based on Big Data Analysis

Computational Psychology to Embed Emotions into Product to Increase Customer Affinity

Multi-graph-Based Intent Hierarchy Generation to Determine Action Sequence

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