A movie recommendation algorithm based on genre correlations

Choi, Sung-Mo; Ko, Sang‐Ki; Han, Yo-Sub

doi:10.1016/j.eswa.2012.01.132

Cited by 129 publications

(67 citation statements)

References 2 publications

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“…Further, categorical classifications (e.g., genres) of the data can address issue of ambiguity in the CF recommender algorithm (i.e., sparseness, cold start, and expert proxy) [40]. For example, a movie has genre information provided by movie experts.…”

Section: System Architecturementioning

confidence: 99%

Modeling User Behaviors to Enable Context-Aware Proactive Decision Support

Newsom

Mittu

Livingston

et al. 2016

Advances in Computer Vision and Pattern Recognition

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The problem of automatically recognizing a user's operational context, the implications of its shifting properties, and reacting in a dynamic manner are at the core of mission intelligence and decision making. Environments such as the OZONE Widget Framework (http://www.owfgoss.org) (OWF) provide the foundation for capturing the objectives, actions, and activities of both the mission analyst and the decision maker. By utilizing a "context container" that envelops an OZONE Application, we hypothesize that both user action and intent can be used to characterize user context with respect to operational modality (strategic, tactical, opportunistic, or random). As the analyst moves from one operational modality to another, we propose that information visualization techniques should adapt and present data and analysis pertinent to the new modality and to the trend of the shift. As a system captures the analyst's actions and decisions in response to the new visualizations, the context container has the opportunity to assess the analyst's perception of the information value, risk, uncertainty, prioritization, projection, and insight with respect to the current context stage. This paper will describe a conceptual architecture for an adaptive work environment for inferring user behavior and interaction within the OZONE framework, in order to provide the decision B. Newsom

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Section: System Architecturementioning

confidence: 99%

Modeling User Behaviors to Enable Context-Aware Proactive Decision Support

Newsom

Mittu

Livingston

et al. 2016

Advances in Computer Vision and Pattern Recognition

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show abstract

“…Using survey data, we will display the many relationships existing within current company data that exists between customer preferences and demographic information to display trends in age, gender and other factors in personal movie preferences that can be used to accurately market specific movies to customers. In general, the user information such as gender, location, or preference is effectively used in movie recommendation systems [7]- [10]. In this paper, we will examine the characteristics of survey respondents who like comedy movies.…”

Section: Movie Genre Preference Prediction Using Machinementioning

confidence: 99%

Movie genre preference prediction using machine learning for customer-based information

Wang

Zhang

2018

2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC)

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Abstract-Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers' demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik-Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers' preferences with a small data set and design prediction tools for these enterprises.

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“…In general, content recommendation would be highly depended on metadata of individual item [1] [3] and user profiles such as preferences, demographical information or history of usage [4]. However, a means for an effective recommendation is often limited, when a set of available metadata of a single data source is not sufficient [2].…”

Section: Using Entertainment Knowledgementioning

confidence: 99%

“…One of fundamental challenges is how they are able to deliver a right content to their users when is needed [1] [2]. There have been a huge number of research efforts for this subject.…”

Section: Introductionmentioning

confidence: 99%

Constructing a Knowledge Base for Entertainment by Interlinking Multiple Data Sources

Kim¹,

Yang²

2017

EAI Endorsed Transactions on Industrial Networks and Intelligen

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This paper describes a knowledge base for entertainment domains, including movies, music, and celebrities. We present an ontology model for representing graph-based knowledge, and describe knowledge processing techniques for constructing an entertainment knowledge base, which is comprised of collection and extraction of large-scale data, and knowledge transformation from them. In particular, we address entity resolution and consolidation techniques for integrating particular entities among heterogeneous data sources. We also demonstrate a mobile application using this knowledge base, which allows users to discover relevant entertainment content based on question & answer system.

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A movie recommendation algorithm based on genre correlations

Cited by 129 publications

References 2 publications

Modeling User Behaviors to Enable Context-Aware Proactive Decision Support

Modeling User Behaviors to Enable Context-Aware Proactive Decision Support

Movie genre preference prediction using machine learning for customer-based information

Constructing a Knowledge Base for Entertainment by Interlinking Multiple Data Sources

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