Introduction to Recommender Systems Handbook

Ricci, Francesco; Rokach, Lior; Shapira, Bracha

doi:10.1007/978-0-387-85820-3_1

Cited by 2,221 publications

(2,296 citation statements)

References 100 publications

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“…In other words, in order to suggest the best learning activities for the user, a content-based recommender system [5], [6] has been integrated in a search engine. In this way, the best resources for the profile, for the context of use and/or for the device are supplied to the user.…”

Section: The E-learning Componentmentioning

confidence: 99%

Distance Education and Social Learning in e-Health

Bitonto¹,

Tria²,

Roselli³

et al. 2014

IJIET

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Abstract-In the context of chronic diseases, in order to guarantee a good quality of life, education is as important as the patient's adherence to a specific therapy. Learning and training processes in these contexts has a dual purpose, on the one hand, to make the patient empowered in order to allow him to recognize the behaviors that might compromise her/his health and, secondly, to train non-specialist medical and paramedical staffs on the procedures for diagnosis and patients follow-up to provide an high quality service. In this context the UBICARE project aims at creating a system that favors the de-hospitalization of patients suffering from peritoneal dialysis and chronic heart failure. The paper describes how the e-learning can be fruitful for patient and medical staff training.

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Section: The E-learning Componentmentioning

confidence: 99%

Distance Education and Social Learning in e-Health

Bitonto¹,

Tria²,

Roselli³

et al. 2014

IJIET

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“…[5] listed six different classes of recommendation approaches, namely content-based approach, collaborative filtering approach, demographic approach, knowledge-based approach, community-based approach and hybrid recommendation approach.…”

Section: Overview Of Recommender Systemsmentioning

confidence: 99%

“…The system also suffers from the cold-start recommendation. According to [5], a cold-start recommendation is when a system is unable provide recommendation to users as users did not provide enough feedback and ratings to compute resemblance to other users. Most of systems that rely on collaborative approach will suffer from cold-start recommendation.…”

Section: Existing Systems and Techniquesmentioning

confidence: 99%

“…According to [5], social-network based recommendations have the same level of accuracy to those derived from traditional approaches except for cases such as in the cold-start situation, where, for instance, users fail to provide enough ratings to compute similarities to others; another case is when the user's ratings are highly varied.…”

Section: Existing Systems and Techniquesmentioning

confidence: 99%

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Document Recommender Agent Based on Hybrid Approach

Chekima¹,

On²,

Alfred³

et al. 2014

IJMLC

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Abstract-As Internet continues to grow, user tends to rely heavily on search engines. However, these search engines tend to generate a huge number of search results and potentially making it difficult for users to find the most relevant sites. This has resulted in search engines losing their usefulness. These users might be academicians who are searching for relevant academic papers within their interests. The need for a system that can assist in choosing the most relevant papers among the long list of results presented by search engines becomes crucial. In this paper, we propose Document Recommender Agent, that can recommend the most relevant papers based on the academician's interest. This recommender agent adopts a hybrid recommendation approach. In this paper we also show that recommendation based on the proposed hybrid approach is better that the content-based and the collaborative approaches.Index Terms-Document recommender agent, agent technology, information retrieval.

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“…The selection of a proper aggregation strategy is a key element in the success of recommendation. The work by Ricci et al [11] describes a series of experiments that were conducted with real users in order to determine which strategy performs best. These experiments show that the average and the average without misery strategies perform best from the users' point of view because they seem to obtain similar recommendations to those that emerge from an actual discussion in a group of "humans".…”

Section: Introductionmentioning

confidence: 99%

Group Recommendation Systems Based on External Social‐Trust Networks

Fang

Jiang

et al. 2018

Wireless Communications and Mobile Computing

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With the development of social networks and online mobile communities, group recommendation systems support users' interaction with similar interests or purposes with others. We often provide some advices to the close friends, such as listening to favorite music and sharing favorite dishes. However, users' personalities have been ignored by the traditional group recommendation systems while the majority is satisfied. In this paper, a method of group recommendation based on external socialtrust networks is proposed, which builds a group profile by analyzing not only users' preferences, but also the social relationships between members inside and outside of the group. We employ the users' degree of disagreement to adjust group preference rating by external information of social-trust network. Moreover, having a discussion about different social network utilization ratio, we proposed a method to work for smaller group size. The experimental results show that the proposed method has consistently higher precision and leads to satisfactory recommendations for groups.

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Introduction to Recommender Systems Handbook

Cited by 2,221 publications

References 100 publications

Distance Education and Social Learning in e-Health

Distance Education and Social Learning in e-Health

Document Recommender Agent Based on Hybrid Approach

Group Recommendation Systems Based on External Social‐Trust Networks

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