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When latent features meet side information: A preference relation based graph neural network for collaborative filtering
As recommender systems shift from rating-based to interaction-based models, graph neural
network-based collaborative filtering models are gaining popularity due to their powerful …
network-based collaborative filtering models are gaining popularity due to their powerful …
[HTML][HTML] Content-based group recommender systems: A general taxonomy and further improvements
Group recommender systems have emerged as a solution to recommend interesting,
suitable, and useful items that are consumed socially by groups of people, rather than …
suitable, and useful items that are consumed socially by groups of people, rather than …
State of art and emerging trends on group recommender system: a comprehensive review
A group recommender system (GRS) generates suggestions for a group of individuals,
considering not only each person's preferences but also factors such as social dynamics …
considering not only each person's preferences but also factors such as social dynamics …
POI recommendation for random groups based on cooperative graph neural networks
Abstract Group Point-of-Interests (POI) recommendation devotes to find the optimal POIs for
groups, which has extracted extensive attention. This work first brings forward a novel POI …
groups, which has extracted extensive attention. This work first brings forward a novel POI …
Deep neural network-based multi-stakeholder recommendation system exploiting multi-criteria ratings for preference learning
A commercially viable multi-stakeholder recommendation system maximizes the utility gain
by learning the personalized preferences of multiple stakeholders, such as consumers and …
by learning the personalized preferences of multiple stakeholders, such as consumers and …
Lagrangian inference for ranking problems
We propose a novel combinatorial inference framework to conduct general uncertainty
quantification in ranking problems. We consider the widely adopted Bradley-Terry-Luce …
quantification in ranking problems. We consider the widely adopted Bradley-Terry-Luce …
Item feature refinement using matrix factorization and boosted learning based user profile generation for content-based recommender systems
A content-based recommender system uses essential item features that play a crucial role in
building quality user preference profiles. However, in most real-world datasets, the item …
building quality user preference profiles. However, in most real-world datasets, the item …
An optimized recommendation framework exploiting textual review based opinion mining for generating pleasantly surprising, novel yet relevant recommendations
Serendipity is a critical factor in the Recommender Systems (RS) in delivering pleasantly
surprising, novel, yet contextually relevant recommendations. Most existing methods …
surprising, novel, yet contextually relevant recommendations. Most existing methods …
[HTML][HTML] Recommendation algorithm using SVD and weight point rank (SVD-WPR)
One of the most prevalent recommendation systems is ranking-oriented collaborative
filtering which employs ranking aggregation. The collaborative filtering study recently …
filtering which employs ranking aggregation. The collaborative filtering study recently …
Performance evaluation of aggregation-based group recommender systems for ephemeral groups
Recommender Systems (RecSys) provide suggestions in many decision-making processes.
Given that groups of people can perform many real-world activities (eg, a group of people …
Given that groups of people can perform many real-world activities (eg, a group of people …