Recommendation system in social networks with topical attention and probabilistic matrix factorization


Autoři: Weiwei Zhang aff001;  Fangai Liu aff001;  Daomeng Xu aff001;  Lu Jiang aff001
Působiště autorů: School of Information Science and Engineering, Shandong Normal University, Jinan, China aff001
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223967

Souhrn

Collaborative filtering (CF) is a common recommendation mechanism that relies on user-item ratings. However, the intrinsic sparsity of user-item rating data can be problematic in many domains and settings, limiting the ability to generate accurate predictions and effective recommendations. At present, most algorithms use two-valued trust relationship of social network to improve recommendation quality but fail to take into account the difference of trust intensity of each friend and user’s comment information. To this end, the recommendation system within a social network adopts topical attention and probabilistic matrix factorization (STAPMF) is proposed. We combine the trust information in social networks and the topical information from review documents by proposing a novel algorithm combining probabilistic matrix factorization and attention-based recurrent neural networks to extract item underlying feature vectors, user’s personal potential feature vectors, and user’s social hidden feature vectors, which represent the features extracted from the user’s trusted network. Using real-world datasets, we show a significant improvement in recommendation performance comparing with the prevailing state-of-the-art algorithms for social network-based recommendation.

Klíčová slova:

Algorithms – Attention – Eigenvectors – Interpersonal relationships – Neural networks – Recurrent neural networks – Social networks – Data mining


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Článek vyšel v časopise

PLOS One


2019 Číslo 10