Abstract:
Event-based Social Network (EBSN) connects online and offline social relationships. EBSNs publish more events, making personalized event recommendation essential to help users choose appealing events. Most event recommendation algorithms fail to distinguish constraint factors from preference factors, which hinders users from attending interested events. To maximize contextual information’s impact on event participation, we distinguish preference and constraint factors and extract soft spatial and temporal constraints from event venue and start time contexts. Then we propose the Preference and Constraint Factor Model (PCFM) based on factorization machine model, using attentive mechanism to weight feature interactions and incorporate latent factors of users and contextual features for personalized perference modeling and event recommendation. PCFM is trained as a ranking model for implicit user feedback using learning-to-rank techniques. Our proposed recommendation model outperforms state-of-the-art event recommendation methods on many metrics in extensive EBSN dataset experiments.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
Did you like this final year project?
To download this project Code with thesis report and project training... Click Here
