Amalgamating Contextual Information into Recommender System

Tiwari, Raj Gaurang; Husain, Mohd. Shahid; Gupta, Bineet Kumar; Agrawal, Anil K.

doi:10.1109/icetet.2010.110

Cited by 22 publications

(8 citation statements)

References 18 publications

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“…Therefore, authors consider that entities with the same structure represent the same entity, and such structure (or syntax, according to the authors), determines the context of data sources. On the other hand, in [60] the contextual information is integrated into a multidimensional recommendation environment, to be used in OLAP systems.…”

Section: Relevant Resultsmentioning

confidence: 99%

Context-based Data Quality Metrics in Data Warehouse Systems

Serra¹,

Marotta²

2017

CLEIej

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The fact that Data Quality (DQ) depends on the context, in which data are produced, stored and used, is widely recognized in the research community. Data Warehouse Systems (DWS), whose main goal is to give support to decision making based on data, have had a huge growth in the last years, in research and industry. DQ in this kind of systems becomes essential. This work presents a proposal for identifying DQ problems in the domain of DWS, considering the different contexts that exist in each system component. This proposal may act as a first conceptual framework that guides the DQ-responsible in the management of DQ in DWS. The main contributions of this work are a thorough literature review about how contexts are used for evaluating DQ in DWS, and a proposal for assessing DQ in DWS through contextbased DQ metrics.

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Section: Relevant Resultsmentioning

confidence: 99%

Context-based Data Quality Metrics in Data Warehouse Systems

Serra¹,

Marotta²

2017

CLEIej

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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%

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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“…Data warehouse provides extreme performance to manage and analyze big data in terms of explicitly finding the new knowledge from the large amount of massive data, including RS [12]. The OLAP and multidimensional data model are used to implement the RS in many applications such as web sites [11], movies [8], and books [10]. Multidimensional data model allows user to view the data in multi aspects.…”

Section: Data Warehouse and Multidimensional Recommender Systemmentioning

confidence: 99%

“…The movie_genres.dat, assigns 20,809 movies with 20 genres. The user_ratedmovies.dat provides 855,598 ratings of 2,113 users on 10,197 movies, which means 8,684 movies have never been rated. The allowed rating scores are 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, and 5, respectively.…”

Section: Data Preparation Using Extract-transform-load (Etl)mentioning

confidence: 99%

“…The traditional content-based RS, if there is inadequate information, RS cannot find enough movies that similar to the target movie, on the other hand, if there are large amount of similar movies, RS requires high computational performance. Another solution for the content-based profiling is to use the data warehouse and multidimensional data model, which has been applied not only in the movie RS [8] but also in the various types of RS such as website [9,10] and book [11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Movie Recommendation Using OLAP and Multidimensional Data Model

Jakkhupan

Kajkamhaeng

2014

Computer Information Systems and Industrial Management

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Part 4: Data Analysis and Information RetrievalInternational audienceThis research proposes an adoption of data warehousing concepts to create a movie recommender system. The data warehouse is generated using ETL process in a desired star schema. The profiles of users and movies are created using multidimensional data model. The data are analyzed using OLAP, and the reports are generated using data mining and analysis tools. The recommended movies are selected using multi-criteria candidate selection. The movies which present the genres that match individual preference are recommended to the particular user. The multidimensional data model and OLAP provide high performance to discover the new knowledge in the big data

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Amalgamating Contextual Information into Recommender System

Cited by 22 publications

References 18 publications

Context-based Data Quality Metrics in Data Warehouse Systems

Context-based Data Quality Metrics in Data Warehouse Systems

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

Movie Recommendation Using OLAP and Multidimensional Data Model

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