Movie Recommendation Using OLAP and Multidimensional Data Model

Jakkhupan, Worapot; Kajkamhaeng, Supasit

doi:10.1007/978-3-662-45237-0_21

Cited by 4 publications

(6 citation statements)

References 10 publications

(15 reference statements)

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“…In addition to the aforementioned recommendation techniques (cf. Section 2.1 ), data warehousing concepts have been utilized for generating recommendations and creating RSs in many applications such as movies [ 15 , 59 ], websites [ 60 ], books [ 61 ], tourism [ 62 ], and Geographical Information Systems (GIS) [ 63 ].…”

Section: Related Workmentioning

confidence: 99%

“…In [ 15 ], the authors utilized data warehousing concepts for creating a movie recommendation system. To recommend a movie to the user, a multi-criteria candidate selection is used, in which movies with genres matching the user’s preferences are recommended.…”

Section: Related Workmentioning

confidence: 99%

“…Data warehousing (DW) is capable of providing extreme performance in managing and analyzing big data by extracting useful insights from massive amounts of data [ 15 ]. DW is defined as a repository that integrates heterogeneous data from multiple data sources into a single multi-dimensional source for the purpose of analyzing and extracting new knowledge from these data.…”

Section: Introductionmentioning

confidence: 99%

“…DW is defined as a repository that integrates heterogeneous data from multiple data sources into a single multi-dimensional source for the purpose of analyzing and extracting new knowledge from these data. Besides the previously mentioned RS techniques, DW techniques have been used for generating recommendations and creating RSs in many applications, such as movies, books, and tourism [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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A Data Warehouse-Based System for Service Customization Recommendations in Product-Service Systems

Esheiba

Helal

Elgammal

et al. 2022

Sensors

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Nowadays, manufacturers are shifting from a traditional product-centric business paradigm to a service-centric one by offering products that are accompanied by services, which is known as Product-Service Systems (PSSs). PSS customization entails configuring products with varying degrees of differentiation to meet the needs of various customers. This is combined with service customization, in which configured products are expanded by customers to include smart IoT devices (e.g., sensors) to improve product usage and facilitate the transition to smart connected products. The concept of PSS customization is gaining significant interest; however, there are still numerous challenges that must be addressed when designing and offering customized PSSs, such as choosing the optimum types of sensors to install on products and their adequate locations during the service customization process. In this paper, we propose a data warehouse-based recommender system that collects and analyzes large volumes of product usage data from similar products to the product that the customer needs to customize by adding IoT smart devices. The analysis of these data helps in identifying the most critical parts with the highest number of incidents and the causes of those incidents. As a result, sensor types are determined and recommended to the customer based on the causes of these incidents. The utility and applicability of the proposed RS have been demonstrated through its application in a case study that considers the rotary spindle units of a CNC milling machine.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Data Warehouse-Based System for Service Customization Recommendations in Product-Service Systems

Esheiba

Helal

Elgammal

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…Nevertheless, numerous OLAP operations (e.g. drill down, slice, roll up and pivot) may confuse the user so it was necessary to design a recommendation system for OLAP sessions that cater to the amateur in order to ease the difficulty in data analysis [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

OLAP4R: A Top-K Recommendation System for OLAP Sessions

2017

KSII TIIS

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The Top-K query is currently played a key role in a wide range of road network, decision making and quantitative financial research. In this paper, a Top-K recommendation algorithm is proposed to solve the cold-start problem and a tag generating method is put forward to enhance the semantic understanding of the OLAP session. In addition, a recommendation system for OLAP sessions called "OLAP4R" is designed using collaborative filtering technique aiming at guiding the user to find the ultimate goals by interactive queries. OLAP4R utilizes a mixed system architecture consisting of multiple functional modules, which have a high extension capability to support additional functions. This system structure allows the user to configure multi-dimensional hierarchies and desirable measures to analyze the specific requirement and gives recommendations with forthright responses. Experimental results show that our method has raised 20% recall of the recommendations comparing the traditional collaborative filtering and a visualization tag of the recommended sessions will be provided with modified changes for the user to understand.

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