Generalized Learning of Neural Network Based Semantic Similarity Models and Its Application in Movie Search

Ye, Xugang; Qi, Zijie; Song, Xinying; He, Xiaodong; Massey, Dan

doi:10.1109/icdmw.2015.34

Cited by 3 publications

(4 citation statements)

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“…Kim and Ha (2014) calculated similarity of matrix vectors of a matrix formed by extracting features of documents from 55 movie scripts. Ye et al (2015) calculated semantic similarity based on movie information, such as release date, title, actors, genre and region. Yi et al (2016) took labels of directors and actors from the Internet Movie Database as source information to determine Jaccard similarity among movies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…et al (2013),Rafsanjani et al (2013),Zhou et al (2014),Kim and Ha (2014),Ye et al (2015),Yi et al (2016),Bougiatiotis and Giannakopoulos (2018),Inan et al (2018) Movies or scripts Second typeBarbosu (2016),Arsan et al (2016) User data Third typeBerlingerio et al (2012),Wang et al (2016),Katarya and Verma (2017),Bag et al (2019),Gazdar and Hidri (2020),Lei and Zhu (2016),Ren et al (2019) …”

mentioning

confidence: 99%

“…used movie information, such as year, genre, celebrity, as source information and determined similarity between movies Kim and Ha (2014). calculated similarity of matrix vectors of a matrix formed by extracting features of documents from 55 movie scripts Ye et al (2015). calculated semantic similarity based on movie information, such as release date, title, actors, genre and region Yi et al (2016).…”

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confidence: 99%

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Predicting total sales volume interval of an experiential product with short life cycle before production: similarity comparison in attribute relationship patterns

Tang

Wang

Cui³

et al. 2021

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PurposeBecause of short life cycle and fluctuating greatly in total sales volumes (TSV), it is difficult to accumulate enough sales data and mine an attribute set reflecting the common needs of all consumers for a kind of experiential product with short life cycle (EPSLC). Methods for predicting TSV of long-life-cycle products may not be suitable for EPSLC. Furthermore, point prediction cannot obtain satisfactory prediction results because information available before production is inadequate. Thus, this paper aims at proposing and verifying a novel interval prediction method (IPM).Design/methodology/approachBecause interval prediction may satisfy requirements of preproduction investment decision-making, interval prediction was adopted, and then the prediction difficult was converted into a classification problem. The classification was designed by comparing similarities in attribute relationship patterns between a new EPSLC and existing product groups. The product introduction may be written or obtained before production and thus was designed as primary source information. IPM was verified by using data of crime movies released in China from 2013 to 2017.FindingsThe IPM is valid, which uses product introduction as input, classifies existing products into three groups with different TSV intervals, mines attribute relationship patterns using content and association analyses and compares similarities in attribute relationship patterns – to predict TSV interval of a new EPSLC before production.Originality/valueDifferent from other studies, the IPM uses product introduction to mine attribute relationship patterns and compares similarities in attribute relationship patterns to predict the interval values. It has a strong applicability in data content and structure and may realize rolling prediction.

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Section: Literature Reviewmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Predicting total sales volume interval of an experiential product with short life cycle before production: similarity comparison in attribute relationship patterns

Tang

Wang

Cui³

et al. 2021

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“…Ye et al,2015 [12] authors proposed a new studying method through a generalized loss characteristic to seize the subtle relevance variations of schooling samples while a extra granular label charter turned into once accessible. Authors have utilized it to the Xbox One's film seek challenge the location consultation-centered individual behavior information changed into as soon as available and the granular relevance differences of coaching samples are derived from the consultation logs.…”

Section: Related Workmentioning

confidence: 99%

Web Interaction Mining Using Adaptive Feature Selection Basedpenta Layered Artificial Neural Network (AFS-Plan) Classifier

Kaviyarasu¹

2017

ijetst

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Predicting the objective of internet users contains different applications in the areas such as e-commerce, entertainment in online, and several internet-based applications. The integral section of classifying the internet queries based on accessible features such as contextual information, keywords and their semantic relationships. This research article aims in proposing Adaptive Feature Selection based Penta Layered Artificial Neural Network Classifier for web interaction mining. Around 31 participants are chosen and given topics to search web contents. Parameters such as precision, recall and F1 score are taken for comparing the proposed AFS-PLANN classifier with the ANN and PLANN. Results proved that the proposed classifier attains better performance than that of the conventional ANN and PLANN.

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NgramSPD: Exploring optimal n-gram model for sentiment polarity detection in different languages

Graovac

Mladenović

Tanasijević³

2019

IDA

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Generalized Learning of Neural Network Based Semantic Similarity Models and Its Application in Movie Search

Cited by 3 publications

References 20 publications

Predicting total sales volume interval of an experiential product with short life cycle before production: similarity comparison in attribute relationship patterns

Predicting total sales volume interval of an experiential product with short life cycle before production: similarity comparison in attribute relationship patterns

Web Interaction Mining Using Adaptive Feature Selection Basedpenta Layered Artificial Neural Network (AFS-Plan) Classifier

NgramSPD: Exploring optimal n-gram model for sentiment polarity detection in different languages

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