Exploiting Rating Abstention Intervals for Addressing Concept Drift in Social Network Recommender Systems

In this chapter, the authors survey the general problem of analyzing a social network in order to make predictions about its behavior, content, or the systems and phenomena that generated it. They begin by defining five basic tasks that can be performed using social networks: (1) link prediction; (2) pathway and community formation; (3) recommendation and decision support; (4) risk analysis; and (5) planning, especially causal interventional planning. Next, they discuss frameworks for using predictive analytics, availability of annotation, text associated with (or produced within) a social network, information propagation history (e.g., upvotes and shares), trust, and reputation data. They also review challenges such as imbalanced and partial data, concept drift especially as it manifests within social media, and the need for active learning, online learning, and transfer learning. They then discuss general methodologies for predictive analytics involving network topology and dynamics, heterogeneous information network analysis, stochastic simulation, and topic modeling using the abovementioned text corpora. They continue by describing applications such as predicting “who will follow whom?” in a social network, making entity-to-entity recommendations (person-to-person, business-to-business [B2B], consumer-to-business [C2B], or business-to-consumer [B2C]), and analyzing big data (especially transactional data) for Customer Relationship Management (CRM) applications. Finally, the authors examine a few specific recommender systems and systems for interaction discovery, as part of brief case studies.

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Toward a rapid development of social network-based recommender systems

IEEE Latin America Transactions ◽

10.1109/tla.2017.7896404 ◽

2017 ◽

Vol 15 (4) ◽

pp. 753-759 ◽

Cited By ~ 4

Author(s):

Gonzalo Rojas ◽

Ismael Garrido

Keyword(s):

Social Network ◽

Recommender Systems ◽

Rapid Development

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Toward Social-Semantic Recommender Systems

International Journal of Information Systems and Social Change ◽

10.4018/ijissc.2016010101 ◽

2016 ◽

Vol 7 (1) ◽

pp. 1-30 ◽

Cited By ~ 4

Author(s):

Dalia Sulieman ◽

Maria Malek ◽

Hubert Kadima ◽

Dominique Laurent

Keyword(s):

Social Network ◽

Collaborative Filtering ◽

Recommender Systems ◽

Semantic Information ◽

Basic Problem ◽

Real Data ◽

Data Sets ◽

The Social ◽

Recommendation Algorithms ◽

Hybrid Recommendation

In this article, the authors consider the basic problem of recommender systems that is identifying a set of users to whom a given item is to be recommended. In practice recommender systems are run against huge sets of users, and the problem is then to avoid scanning the whole user set in order to produce the recommendation list. To cope with problem, they consider that users are connected through a social network and that taxonomy over the items has been defined. These two kinds of information are respectively called social and semantic information. In their contribution the authors suggest combining social information with semantic information in one algorithm in order to compute recommendation lists by visiting a limited part of the social network. In their experiments, the authors use two real data sets, namely Amazon.com and MovieLens, and they compare their algorithms with the standard item-based collaborative filtering and hybrid recommendation algorithms. The results show satisfying accuracy values and a very significant improvement of performance, by exploring a small part of the graph instead of exploring the whole graph.

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