On social networks and collaborative recommendation

By Ioannis Konstas, Vassilios M. Stathopoulos, Joemon M. Jose. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2009

682 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/f13c0ff39ff491d45a46bb2414ca8030643d778d

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In Information Systems

Citations in the corpus

Do social explanations work?: studying and modeling the effects of social explanations in recommender systems (2013, background): “cial connection information have been proposed to support collaborating filtering algorithms that use social information [12,15], focusing on preferences in users’ immediate social networks [10, 20] and computing trust between people in networks [9] to improve recommendations.”
Explainable recommendation with fusion of aspect information (2018, methodology): “Collaborative Filtering (CF) based approaches [4, 15, 34, 38] make rating prediction using a decomposition of a user-item rating matrix, and fall into two categories, the memory based CF [6, 11, 12] and the model based CF [16, 17, 31].”
SemRec: A Semantic Enhancement Framework for Tag Based Recommendation (2011, methodology): “(Konstas, Stathopoulos, and Jose 2009) adopted Random Walk with Restart to model the social tagging in a music track recommendation system.”
Discussion Graphs: Putting Social Media Analysis in Context (2014, methodology): “For example, social media analysis methods have been used in social search and ranking (Weng et al. 2010), recommender systems (Konstas, Stathopoulos, and Jose 2009; Feng and Wang 2012), media summarization (Lin et al. 2012), and so on.”
Time-aware point-of-interest recommendation (2013, background): “Recall that both the Foursquare and Gowalla datasets have a low density, which usually results in relatively low precision and recall values [10,23].”
Exploiting geographical influence for collaborative point-of-interest recommendation (2011, methodology): “Recent studies have argued that social friends tend to share common interests and thus can be used in the process of collaborative filtering for making recommendations [11, 12, 2, 13, 24].”
Music recommendation by unified hypergraph: combining social media information and music content (2010, methodology): “[19] create a collaborative recommender system which constructs a social graph over users, tags and resources.”
Exploring social influence for recommendation: a generative model approach (2012, methodology): “, friends share some common interests) and propose to exploit the correlation for item recommendations [12, 16, 17, 18, 19, 6].”
Collaborative Filtering beyond the User-Item Matrix (2014, methodology): “Pioneering work in this area was presented by Konstas et al. [2009], who proposed using random walk with restarts to mine relationships in a multi-partite graph including users, items, and tags.”
Graph-based term weighting for information retrieval (2012, background): “graph theoretic approaches to IR is in the context of social or collaborative networks and recommender systems (Craswell and Szummer 2007; Kleinberg 2006; Konstas et al. 2009; Noh et al. 2009; Schenkel et al. 2008).”
Comparing the Predictive Capability of Social and Interest Affinity for Recommendations (2014, methodology): “A common practice in evaluating such approaches is to resort to (i) one [42, 45, 35, 22, 20, 25, 30], sometimes two [39, 31] datasets and (ii) global averages for the metrics of choice.”
Cross-domain collaboration recommendation (2012, background): “[17] investigated how social relationships can help recommendation.”
Ieee Transactions on Knowledge and Data Engineering, Manuscropt Id 1 Social Recommendation with Cross-domain Transferable Knowledge (neutral): “Frameworks exist that connect directly-related item domains, such as a music album and tags on that album [3], or web pages and queries to them [4].”
Social Collaborative Viewpoint Regression with Explainable Recommendations (2017, methodology): “Trusted social relations have been applied to enhance the performance of collaborative filtering [7, 18, 35].”
Machine Learning on Graphs: A Model and Comprehensive Taxonomy (2020, background): “Graphs are a universal data structures that can represent complex relational data (composed of nodes and edges), and appear in multiple domains such as social networks, computational chemistry [41], biology [105], recommendation systems [64], semi-supervised learning [39], and others.”
Location recommendation for location-based social networks (2010, background): “(c) 2010ACM ISBN 978-1-4503-0428-3/10/11...$10.00. networking services allow users to connect with friends, explore places (e.g., restaurants, stores, cinema theaters, etc), share their locations, and upload photos, video, and blogs.”
Social media recommendation based on people and tags (2010, background): “Several studies suggest incorporating explicit social network information in CF systems to improve the quality of recommendation in domains such as movies and books (e.g., [3,12,30]), music [20], clubs [14], and news stories [21].”