Temporal diversity in recommender systems

By Neal Kiritkumar Lathia, S. Hailes, Licia Capra, Xavier Amatriain. 2010

328 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Recommender Systems

How researchers see this paper

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

In Information Systems

1. Julian McAuley
2. Ruining He
3. Dietmar Jannach
4. Bart P. Knijnenburg
5. Michael Jugovac
6. Mozhgan Karimi
7. Sophie Ahrens
8. Pablo Castells

In Management Science and Operations Research

1. Dietmar Jannach
2. Julian McAuley
3. Ruining He
4. Michael Jugovac
5. Mozhgan Karimi
6. Sophie Ahrens
7. Pablo Castells
8. Saul Vargas

In Recommender Systems

1. Ruining He
2. Julian McAuley
3. Dietmar Jannach
4. Michael Jugovac
5. Neil J. Hurley
6. Martijn C. Willemsen
7. Bart P. Knijnenburg
8. Sophie Ahrens

Citations in the corpus

• Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering (2016, background): “Previous models have considered either visual [12, 14] or temporal data [5, 19, 23, 39] in isolation, though few have modeled both aspects simultaneously as we do here.”
• Exposure diversity as a design principle for recommender systems (2018, background): “In a similar sense, Zhang and Hurley (2008) and Lathia et al. (2010) have argued that the lack of variation can also have a frustrating effect on users, while Schönbach (2007) argued that people actually buy news media because they are interested in what he calls ‘ reliable surprises ’ .”
• Rank and relevance in novelty and diversity metrics for recommender systems (2011, background): “All previous recommendations 〈 〉 Novelty Self-system novelty [15]”
• Novelty and Diversity in Recommender Systems (2015, background): “All previous recommendations 〈 〉 Novelty Self-system novelty [15]”
• News recommender systems - Survey and roads ahead (2018, background): “Besides the question of how to balance diversity with accuracy, researchers investigated to what extent the level of diversification should be determined by the preferences of the individual user [162] or how diversity preferences can depend on time aspects [102].”
• Diversity in Big Data: A Review (2017, neutral): “9,15,46 ).”
• Recommender Systems (2016, neutral): “A temporal framework for realistic evaluation is discussed in [335].”
• Recommender Systems (2012, background): “Therefore, temporal diversity [292] becomes crucial in designing time-based algorithms.”
• Evaluating Recommender Systems with User Experiments (2015, background): “A steady stream of research has considered algorithmic solutions to diversifying recommendations [1, 76, 115, 124, 125].”