How to Get the Recommender Out of the Lab?

Picault, Jérôme; Ribière, Myriam; Bonnefoy, David; Mercer, Kevin

doi:10.1007/978-0-387-85820-3_10

Cited by 20 publications

(14 citation statements)

References 35 publications

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“…Their design and implementation entail understanding the type of users and the type of data available to describe the items. The explanations given here may not be sufficient for a beginner to learn the techniques for designing RSs, but the references above and the work of Picault et al [31] could give the required skills on how to get RSs out of the lab.…”

Section: Methodsmentioning

confidence: 98%

A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems

Hassan

Hamada

2017

Applied Sciences

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Accuracy improvement has been one of the most outstanding issues in the recommender systems research community. Recently, multi-criteria recommender systems that use multiple criteria ratings to estimate overall rating have been receiving considerable attention within the recommender systems research domain. This paper proposes a neural network model for improving the prediction accuracy of multi-criteria recommender systems. The neural network was trained using simulated annealing algorithms and integrated with two samples of single-rating recommender systems. The paper presents the experimental results for each of the two single-rating techniques together with their corresponding neural network-based models. To analyze the performance of the approach, we carried out a comparative analysis of the performance of each single rating-based technique and the proposed multi-criteria model. The experimental findings revealed that the proposed models have by far outperformed the existing techniques.

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Section: Methodsmentioning

confidence: 98%

A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems

Hassan

Hamada

2017

Applied Sciences

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“…Studies conducted for news recommender systems like MESH project [4] concluded that these have two different ways of usage: pull mode -the recommendations are driven by the user queries, and push mode -the recommendations can be offered even if the user did not express any request for a specific item. Because of this dual mode, the choice over the implementation of recommendation algorithm in the first case would be towards content based solutions, whilst in the second case towards collaborative filtering.…”

Section: State Of the Art In The Fieldmentioning

confidence: 99%

“…As in other news recommender systems [4], our system is employing different recommendation algorithms, case the user is requiring or not for suggestions (pull-push mode). If the selection of data sources is directed by the user (it is subject to the pull mode), then the recommendation algorithm best suited is one finding items similar to those directly expressed by user (a content based approach).…”

Section: Data Sourcesmentioning

confidence: 99%

Ontology Importance towards Enhancing Suggestions in a News Recommender System for a Financial Investor

Bresfelean

Ghiran

2011

Metadata and Semantic Research

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Abstract. In terms of the current information society, where news and information generally run at a remarkable speed, it is imperative that the events of capital importance for the world economy (and others fields) to be available and disseminated in real time. Thus, we refer to news that may influence in a large manner the stock quotes, forex, oil quotes and so on. In view of the fact that the Internet abounds in news, in our on-going research (under grant TE 316 "Intelligent methods for decision fundamentation on stock market transactions, based on public information") we seek to propose a solution for extracting news vital information, its interpretation in real time, followed by suggesting possible decisional alternatives to the representatives involved in the stock exchange market. We put forward in the present paper our starting ideas for a conceptual model for a semantic Web news search, which encompasses multi-agent technologies, semantic Web, text search and mining, RSS news feeds.

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“…News recommendation is a domain where content-based methods find their natural application. MESH (Multimedia sEmantic Syndication for enHanced news Services) is a research project that designed a framework for intelligent creation and delivery of semantically-enhanced multimedia news information [ 5 ]. Another interesting domain of application is recommendation of financial investment strategies, a complex and knowledge-intensive task where filtering techniques are recently used [ 6 ].…”

Section: Motivations and Related Researchmentioning

confidence: 99%

DECiSION: Data-drivEn Customer Service InnovatiON

Esposito

Polignano

Basile

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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The paper presents DECiSION, an innovative framework in the field of Information Seeking Support Systems, able to retrieve all the data involved in a decision-making process, and to process, categorize and make them available in a useful form for the ultimate purpose of the user request. The platform is equipped with natural language understanding capabilities, allowing the interpretation of user requests and the identification of information sources from which to independently retrieve the information needed for the sensemaking task. The project foresees the implementation of a chatbot, which acts as a virtual assistant, and a conversational recommender system, able to dialogue with the user to discover their preferences and orient their answers in a personalized way. The goal is therefore to create an intelligent system to answer autonomously and comprehensively questions posed in natural language about a specific reference domain, to support the decision-making process. The paper describes the general architecture of the framework and then focuses on the key component that automatically translate the natural language user query into a machine-readable query for the service repository.

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How to Get the Recommender Out of the Lab?

Cited by 20 publications

References 35 publications

A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems

A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems

Ontology Importance towards Enhancing Suggestions in a News Recommender System for a Financial Investor

DECiSION: Data-drivEn Customer Service InnovatiON

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