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3T-IEC*: a context-aware recommender system architecture for smart social networks (EBSN and SBSN)

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Abstract

Recommending Smart Social Network (SSN) items which are in line with user preferences is one of the significant applications in SSNs such as Event-Based Social Networks (EBSNs) and Smart object Based Social Network (SBSN). It is well acknowledged that geographical and social contextual influences play an important role in SSN item Recommender System (RS). Whereas incorporating such contextual influences into SSN item RSs is a challenging issue which needs to be addressed along with the conventional RS challenges (cold-start problem and data sparsity). To this end, a novel SSN item recommendation architecture (named as 3T-IEC*) based on Graph Convolutional Network (GCN) is proposed. Specifically, 3T-IEC* constructs an SSN item location graph based on physical distance between two SSN items and leverages a GCN-based representation learning method to capture geographical influence of SSN items; which not only incorporates the geographical preferences but also delivers a convincing idea to solve the data sparsity problem. Sequentially, 3T-IEC* deploys one more GCN to incorporate users’ social associations. Furthermore, 3T-IEC* leverages self-attention technique with multiple heads to incorporate user’s preferences over SSN items’ multiple features. The experimental results on four real life datasets demonstrate superior performance of 3T-IEC* compared with state-of-the-art methods such as SkyLine (149%), 3T-IEC (54%), DeepWalk (38%), SORec (9%), and NGCF (7%).

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The datasets used in the current study are publicly available.

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    Pratibha Mahajan & Pankaj Deep Kaur

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Mahajan, P., Kaur, P.D. 3T-IEC*: a context-aware recommender system architecture for smart social networks (EBSN and SBSN). J Intell Inf Syst 60, 199–233 (2023). https://doi.org/10.1007/s10844-022-00743-3

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