AAIN: Attentional aggregative interaction network for deep learning based recommender systems

He, Hong; Ruixi, Zhang; Zhang, Yangsen; Ren, Jiadong

doi:10.1016/j.neucom.2023.126374

Cited by 2 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some computer science methods proposed and applied to recommender systems by authors in previous studies include particle swam optimization, deep reinforcement learning, natural language processing, knowledge graph, collaborative filtering and genetic algorithm [20][21][22][23][24][25][26][27][28]. Others include hybrid deep learning and other artificial intelligence models such as fuzzy logic recommender systems [29][30][31].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Social Filtering Model for Event Recommendation Services

Oyemade,

Aworonye

2024

AJAI

View full text Add to dashboard Cite

In the contemporary time, technology has made the determination and discovery of human preferences, priorities and personal inclinations possible through the use of recommender systems. Activities of users on the internet can be monitored, extracted, stored, analyzed and used by the recommender systems for suggesting future events to users on the web. This paper aims at developing and analyzing a model for event services recommendation for visitors to events. Event seekers, organizers and event service providers get notified, plan and book for upcoming events from their comfort zones without hassles of gallivanting nooks and crannies to enquire about prospective events. There is not any compelling need to interface with under-enthusiasts and intermediaries in the course of organizing, visiting and providing services for an event. However, it is obvious that massive amount of available information on the web exhibit frustrating attributes, hence it is increasingly a difficult task for users to find the content of interest; in other words, a huge chunk of information undiscovered on the network is left behind as “dark information”. In context, event service recommendation uses deep learning social filtering base techniques which adopt similarity computation measures with a bias for Pearson correlation coefficient, cosine similarity, and Euclidean similarity to recommend related and most relevant events/services to the targeted online audience. In this paper, the aim is to develop a deep learning model which integrates social filtering technique for enhancing the quality of event recommendation for users. A model based on the deep learning algorithm of multilayered perceptron and Neural Collaborative Filtering is proposed for event recommender services. The results from various simulations using meetup website dataset shows that the proposed model performs better than other techniques. The results yield 70% accuracy, 66% precision and 98% recall.

show abstract