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## Efficient and socio-aware recommendation approaches for bigdata networked systems

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In this chapter, we present several approaches designed for providing efficient recommendations in large web systems characterized by bigdata scales. The key feature of the considered approaches is that they all rely on different elements and properties of social/complex network analysis for addressing various deficiencies of legacy and current recommendation systems when very large operational scales emerge. The main challenges of recommendations addressed by the presented approaches are the diversity (novelty) of recommendations, the cold-start problem, scalability and noise filtering issues, as well as the efficiency of developing these approaches and integrating them in operational systems. This chapter aspires to provide an educated overview, leading to a solid fundamental background on how social/complex network analysis can be exploited for more effective recommendations in stringent environments characterized by large scales ofusers, items and associated data, cumulatively referred to as big network data. Furthermore, our work aims at highlighting the design principles that are more interesting for enabling the extension of the presented approaches and their combination with other current state-of-the-art techniques, thus leading to more socioaware and efficient recommendation approaches in the near and longer term future.

Chapter Contents:

• 4.1 Introduction
• 4.2 Background on recommendation systems and social network analysis
• 4.2.1 Recommendation systems
• 4.2.2 Social network analysis
• 4.3 Socio-aware recommendation systems
• 4.3.1 Hyperbolic path-based recommendation system
• 4.3.2 Probabilistic graphical models
• 4.3.2.1 Pairwise recommendation MRFs
• 4.3.2.2 Preference networks
• 4.3.2.3 Ordinal random fields
• 4.3.2.4 Preference Markov random fields
• 4.3.3 Information diffusion-aware recommendation approaches
• 4.3.4 Context-based recommendations in pervasive systems
• 4.3.4.1 Context-aware recommendation systems
• 4.3.4.2 Context-aware recommendation systems in IoT ecosystem
• 4.3.4.3 Context-aware recommendation systems and social networks
• 4.4 Qualitative comparison
• 4.5 Open problems and conclusion
• References

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