A probabilistic model for using social networks in personalized item recommendation
Preference-based recommendation systems have transformed how we consume media. By
analyzing usage data, these methods uncover our latent preferences for items (such as …
analyzing usage data, these methods uncover our latent preferences for items (such as …
Social network based recommendation systems: A short survey
This paper examines the background of the recommender systems and the state-of-art
technologies in this current research area. We examined the needs for recommendation …
technologies in this current research area. We examined the needs for recommendation …
Social group recommendation in the tourism domain
Recommender Systems learn users' preferences and tastes in different domains to suggest
potentially interesting items to users. Group Recommender Systems generate …
potentially interesting items to users. Group Recommender Systems generate …
A social influence approach for group user modeling in group recommendation systems
While many studies on typical recommender systems focus on making recommendations to
individual users, social activities usually involve groups of users. Issues related to group …
individual users, social activities usually involve groups of users. Issues related to group …
A social formalism and survey for recommender systems
This paper presents a general formalism for Recommender Systems based on Social
Network Analysis. After introducing the classical categories of recommender systems, we …
Network Analysis. After introducing the classical categories of recommender systems, we …
Social influence in signed networks
X He, J Lu, H Du, X Jin - IEEE Transactions on Computational …, 2023 - ieeexplore.ieee.org
Social influence has been widely discussed in various disciplines due to its important
sociological significance. However, the dynamics of social influence in signed networks …
sociological significance. However, the dynamics of social influence in signed networks …
Collaboration for success in crowdsourced innovation projects: Knowledge creation, team diversity, and tacit coordination
When innovation projects are crowdsourced, individuals are allowed to form teams and
collaborate to develop a successful solution. In this environment, teams will be competing …
collaborate to develop a successful solution. In this environment, teams will be competing …
Assessing the effects of expanded input elicitation and machine learning-based priming on crowd stock prediction
H Bhogaraju, A Jain, J Jaiswal… - International Conference …, 2023 - Springer
The stock market is affected by a seemingly infinite number of factors, making it highly
uncertain yet impactful. A large determinant of stock performance is public sentiment, which …
uncertain yet impactful. A large determinant of stock performance is public sentiment, which …
Introducing the space recommender system: how crowd-sourced voting data can enrich urban exploration in the digital era
M Traunmueller, A Fatah gen. Schieck - Proceedings of the 6th …, 2013 - dl.acm.org
Navigation systems like Google Maps and TomTom are designed to generate the shortest
and less time consuming path for the user to reach a certain destination from his origin …
and less time consuming path for the user to reach a certain destination from his origin …
Dynamic matrix factorization with social influence
AY Aravkin, KR Varshney… - 2016 IEEE 26th …, 2016 - ieeexplore.ieee.org
Matrix factorization is a key component of collaborative filtering-based recommendation
systems because it allows us to complete sparse user-by-item ratings matrices under a low …
systems because it allows us to complete sparse user-by-item ratings matrices under a low …