Improving Collaborative Filtering Algorithms

Kharrat, Firas Ben; Elkhleifi, Aymen; Faiz, Rim

doi:10.4018/ijksr.2016070107

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…In particular, [74] proposed a tourist system that matches the user's location with the top-k recommendations through a linear distance for the contents and the CS for the relationship between the user profiles. Also, [75] developed an approach to extract information from users' preferences of a website, established the similarity of users, and generated a tourist attraction with the Slope One algorithm. Considering the problem of cold start and the scarcity of the CF algorithms' information, in [76] developed an architecture of a deep neural network based on an MF of latent characteristics of the project developers, their tasks, and their relationships.…”

Section: Collaborative Filteringmentioning

confidence: 99%

Tourist Recommender Systems Based on Emotion Recognition—A Scientometric Review

2020

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Recommendation systems have overcome the overload of irrelevant information by considering users’ preferences and emotional states in the fields of tourism, health, e-commerce, and entertainment. This article reviews the principal recommendation approach documents found in scientific databases (Elsevier’s Scopus and Clarivate Web of Science) through a scientometric analysis in ScientoPy. Research publications related to the recommenders of emotion-based tourism cover the last two decades. The review highlights the collection, processing, and feature extraction of data from sensors and wearables to detect emotions. The study proposes the thematic categories of recommendation systems, emotion recognition, wearable technology, and machine learning. This paper also presents the evolution, trend analysis, theoretical background, and algorithmic approaches used to implement recommenders. Finally, the discussion section provides guidelines for designing emotion-sensitive tourist recommenders.

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Section: Collaborative Filteringmentioning

confidence: 99%

Tourist Recommender Systems Based on Emotion Recognition—A Scientometric Review

2020

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Algorithm Optimization for Cold Start of Collaborative Filtering System

Wang

Yang

Zhao

2020

J. Phys.: Conf. Ser.

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With the development of network technology, the amount of information on the network has grown and expanded rapidly with an exponential law, and its information organization is heterogeneous, diverse, and distributed. With the vigorous development of Internet information services, the scale of its information resources has also exploded. For ordinary users, the problems of “information trek” and “information overload” on the Internet are becoming increasingly serious. In order to solve the problem of information overload, recommendation system has become an indispensable tool for today’s e-commerce platform, which can help users find valuable information quickly. Collaborative filtering-based recommendation algorithms have been widely applied and studied in recommendation systems. Although the collaborative filtering algorithm has been widely used, there are still problems such as data sparsity, scalability, and cold start, which seriously limit the quality of recommendations. Therefore, collaborative filtering algorithms face many challenges. Especially recommendation systems using collaborative filtering technology. This article discusses the cold start system, the cold start user, and the cold start scenario. Effective use of project content information and user personal information is one of the effective methods to solve the cold start problem, that is, a hybrid recommendation technology combining information filtering and collaborative filtering. This paper proposes a hybrid recommendation technology that can better solve the cold start problem.

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SMART Pedagogy

Daniela

Lytras

Learning Strategies and Constructionism in Modern Education Settings

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In this chapter, the authors would like to discuss topical issues in pedagogical science related to the learning process in a technology and media-enriched environment, opening up discussions on the development of pedagogical science's sector – smart pedagogy to promote a synergy between technology and pedagogy in the context of higher education. The authors' ideas on the concept of smart pedagogy, opened discussion on domains of teachers'role in the discourse of smart pedagogy. and domains of smart pedagogy as a new branch of pedagogy are discussed.

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Improving Collaborative Filtering Algorithms

Cited by 4 publications

References 32 publications

Tourist Recommender Systems Based on Emotion Recognition—A Scientometric Review

Tourist Recommender Systems Based on Emotion Recognition—A Scientometric Review

Algorithm Optimization for Cold Start of Collaborative Filtering System

SMART Pedagogy

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