Tag-aware recommender systems by fusion of collaborative filtering algorithms

By Karen H. L. Tso-Sutter, Leandro Bezerra Marinho, Lars Schmidt-Thieme. ACM Symposium on Applied Computing, 2008

431 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/33bb3b15729dba3f74dd6bbc36e792f1903a6207

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Fernando Ortega
2. Antonio Hernando
3. Jesús Bobadilla
4. Shulong Tan
5. Yue Shi
6. Abraham Gutiérrez
7. Jiajun Bu
8. Martha A. Larson

In Information Systems

1. Yue Shi
2. Fernando Ortega
3. Antonio Hernando
4. Abraham Gutiérrez
5. Jesús Bobadilla
6. Jiajun Bu
7. Martha A. Larson
8. Stathes Hadjiefthymiades

Citations in the corpus

• Tag-Aware Personalized Recommendation Using a Hybrid Deep Model (2017, background): “Consequently, many efforts have been put on tag-aware personalized recommendation using content-based filtering [Cantador et al. , 2010; Shepitsen et al. , 2008] or collaborative filtering [Bouadjenek et al. , 2013; Tso-Sutter et al. , 2008].”
• Recommender Systems (2016, neutral): “The method in [603] works with implicit feedback data sets in which the ratings matrices are assumed to be unary.”
• Collaborative Filtering beyond the User-Item Matrix (2014, background): “Tags are recognized as an information source that can be highly beneficial for improving the performance of recommender systems [Sen et al. 2009; Tso-Sutter et al. 2008; Zhen et al. 2009].”
• Recommender systems survey (2013, methodology): “[219] propose a generic method that allows tags to be incorporated to standard CF algorithms.”
• A Distributed, Architecture-Centric Approach to Computing Accurate Recommendations from Very Large and Sparse Datasets (2011, background): “be described in a set of features that are either manually assigned or automatically extracted from machine-readable content [50, 58, 161].”
• Facing the cold start problem in recommender systems (2014, methodology): “The fusion-based methods (Tso-Sutter et al., 2008; Zhang et al., 2010; Oku & Hattori, 2011) adopt information fusion techniques for building the final items list.”
• A review on deep learning for recommender systems: challenges and remedies (2018, background): “2010), tag-aware recommender systems integrate product tags to standard CF algorithms (Tso-Sutter et al. 2008), trust-based recommender systems take the trust relationship among users into account (Bedi et al.”
• Recommender Systems (2012, methodology): “Due to the success of collaborative filtering, many works were devoted to using tags to measure similarity among users or objects, and then fuse with the standard memory-based collaborative filtering framework [273, 274, 275].”
• Tag-Aware Personalized Recommendation Using a Deep-Semantic Similarity Model with Negative Sampling (2016, methodology): “Collaborative systems recommend to users items liked by similar users using machine learning techniques, such as nearest neighbor modeling [9] and matrix factorization [1].”
• Content-based Recommender Systems: State of the Art and Trends (2011, neutral): “assisting users for annotation purposes) [97, 94].”
• Music recommendation by unified hypergraph: combining social media information and music content (2010, background): “[39] reduce three types of objects in tagging relations (users, resources and tags) to two types by treating tags as either users or resources, and then apply traditional item-based or user-based collaborative filtering algorithms [1], respectively.”