Bayesian recommender system for social information sharing: Incorporating tag-based personalized interest and social relationships

Huang, Cheng-Lung

doi:10.3233/ida-183910

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…These are based on the assumption that users have the same interests if their contexts are similar. The technique presented in [80] provides information filtering recommendations based on social relationships and tag-based interests. The model proposed in [81] takes the title and abstract of a research paper as inputs and recommends the potential top venues among journals and conferences to help researchers choose the suitable venue for publishing their research.…”

Section: ) Multidimensional-based Recommender Systemmentioning

confidence: 99%

“…Table III shows the distribution of studies based on the different techniques that are used in recommendation approaches. Techniques References Bayesian probability [80], [90] Bipartite graphs embedding [84] Clustering [30], [60], [70], [91], [107] Decision tree [26], [38], [83] Deep neural networks [35], [56], [59], [65], [71], [72], [73], [76], [93], [98], [99], [100], [108] Distance-based method [88], [89] Fuzzy clustering [113] K Nearest Neighbor [24], [25], [44], [54], [75]…”

Section: Sparsitymentioning

confidence: 99%

“…Though, social network hybrid filtering systems overcome CB and CF's limitations by combining ratingfeature data of user-item profiles. The social network recommender system presented in [80] is a hybrid system that applies collaborative filtering and content-based recommendation. The system uses the nearest neighbors' technique as a collaborative phase on the target user to discover closest neighbors while uses a modified Bayesian probability scheme to generate recommendations.…”

Section: ) Hybrid Approachmentioning

confidence: 99%

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Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Suhaim

Berri

2021

IEEE Access

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Context-aware recommender systems dedicated to online social networks experienced noticeable growth in the last few years. This has led to more research being done in this area stimulated by the omnipresence of smartphones and the latest web technologies. These systems are able to detect specific user needs and adapt recommendations to actual user context. In this research, we present a comprehensive review of context-aware recommender systems developed for social networks. For this purpose, we used a systematic literature review methodology which clearly defined the scope, the objective, the timeframe, the methods, and the tools to undertake this research. Our focus is to investigate approaches and techniques used in the development of context-aware recommender systems for social networks and identify the research gaps, challenges, and opportunities in this field. In order to have a clear vision of the research potential in the field, we considered research articles published between 2015 and 2020 and used a research portal giving access to major scientific research databases. Primary research articles selected are reviewed and the recommendation process is analyzed to identify the approach, the techniques, and the context elements employed in the development of the recommendation systems. The paper presents the detail of the review study, provides a synthesis of the results, proposes an evaluation based on measurable evaluation tools developed in this study, and advocates future research and development pathways in this interesting field.

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Section: ) Multidimensional-based Recommender Systemmentioning

confidence: 99%