Temporal collaborative filtering with adaptive neighbourhoods

By Neal Kiritkumar Lathia, S. Hailes, Licia Capra. 2009

128 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research

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. Sophie Ahrens
2. Liang Wang
3. J. Konstan
4. John Riedl
5. Tien-Ping Tan
6. Qiang Liu
7. Li Chen
8. Shu Wu

In Information Systems

1. Sophie Ahrens
2. Charu C. Aggarwal
3. John Riedl
4. J. Konstan
5. Michael D. Ekstrand
6. Li Chen
7. Yongfeng Zhang
8. Julian McAuley

In Management Science and Operations Research

1. Sophie Ahrens
2. John Riedl
3. J. Konstan
4. Li Chen
5. Michael D. Ekstrand
6. Pedro G. Campos
7. Fernando Díez
8. Iván Cantador

Citations in the corpus

• From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews (2013, background): “Around the same time, ‘adaptive neighborhood’ models were proposed [24], that address the problem of iteratively training recommender systems whose parameters ought to change over time.”
• Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols (2013, methodology): “Finally, Lathia et al. (2009) showed that improvements obtained by some non-contextualized algorithms on the Netflix Prize probe set1 do not hold when computing predictions on an iterative basis by strictly using past ratings to predict future ratings—the actual setting for a real-world recommender…”
• Recommender Systems (2016, unknown): “Another technique that uses adaptive neighborhoods for temporal collaborative filtering is discussed in [333].”
• Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts (2016, methodology): “Time-aware neighborhood based methods (Ding and Li 2005; Nasraoui et al. 2007; Lathia, Hailes, and Capra 2009; Liu et al. 2010) adapt the ordinary neighborhood based algorithms to temporal effects via giving more relevance to recent observations and less to past observations.”
• Recommending Groups to Users Using User-Group Engagement and Time-Dependent Matrix Factorization (2016, methodology): “Other authors have examined time-dependent methods such as tensor factorization, session-based temporal graph model and dynamic matrix factorization etc. (Xiong et al. 2010; Xiang et al. 2010; Koenigstein, Dror, and Koren 2011; Lathia, Hailes, and Capra 2009; Chua, Oentaryo, and Lim 2013).”
• Recommender Systems (2012, methodology): “Similar methods used the time factor to adaptively choose temporal neighborhoods and then obtain recommendations via the refined neighbors [293–295].”
• Collaborative Filtering Recommender Systems (2011, methodology): “[86] propose dynamically adapting the neighborhood size used for each user to minimize the error in their”
• Online Machine Learning in Big Data Streams (2018, background): “The difficulty of evaluating streaming recommenders was first mentioned in [148], although the authors evaluated models by offline training and testing split.”
• Temporal recommendation on graphs via long- and short-term preference fusion (2010, result): “Lathia [11] formalized CF as a time-dependent iterative prediction problem, and found that certain prediction methods that improve prediction accuracy in the Netflix do not show similar improvement over a set of iterative train-test experiments with growing data.”
• Temporal diversity in recommender systems (2010, background): “Similarly, our previous work looks at how changes are further reflected in the global rating median and mode [4].”