Avoiding monotony: improving the diversity of recommendation lists

By Mi Zhang, Neil J. Hurley. ACM Conference on Recommender Systems, 2008

538 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/a72f412d336d69a82424cb7d610bb1bb9d81ef7c

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In Artificial Intelligence

In Information Systems

In Management Science and Operations Research

Citations in the corpus

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].”
Managing Popularity Bias in Recommender Systems with Personalized Re-ranking (2019, background): “And ﬁnally, there is substantial research in recommendation diversity, where the goal is to avoid recommending too many similar items (Zhou et al. 2010; Castells, Vargas, and Wang 2011; Zhang and Hurley 2008).”
SCENE: a scalable two-stage personalized news recommendation system (2011, result): “To evaluate how diverse our recommendation result is, we compare the set diversity described in [26] between the results of SCENE and other recommender systems.”
Collaborative prediction and ranking with non-random missing data (2009, background): “Research on recommender systems has focused almost exclusively on properties of rating prediction and ranking methods including prediction accuracy [1], ranking accuracy [15], novelty and diversity [17, 16], and explainability [5].”
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques (2012, background): “, an average dissimilarity between all pairs of items recommended to a given user) [8], [33], [46], [54], [57].”
Novelty and Diversity in Recommender Systems (2015, background): “Being similar to the profile, recommended items are also similar among themselves, which explains the low EILD.”
Ranking with Fairness Constraints (2017, background): “…w ij s to attempt to capture the amount of diversity item i would introduce at position k conditioned on the items that were placed at positions 1 , . . . , k − 1 (see [CG98, ZH08, CKC + 08, ZCL03, ZMKL05]), or casting it directly as an (unconstrained) multi-objective optimization problem [YS16].”
Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity (2017, methodology): “ILAD and ILMD are monotonously decreasing as θ increases.”
Exposure diversity as a design principle for recommender systems (2018, background): “However, more recent studies (Zhang & Hurley, 2008; Zhou et al., 2010) have observed that this type of approach might lead to a situation where the recommendations offered are ‘ monothematic ’ and repetitive and therefore end up being boring to the user.”
Search result diversification (2010, methodology): “Finally, in [18], where the diversification problem is formulated as an optimization one, a solution is approximated via optimization techniques that include problem relaxation and quantization.”
Evaluating Recommendation Systems (2011, background): “As diversity may come at the expanse of other properties, such as accuracy [62], we can compute curves to evaluate the decrease in accuracy vs.”
Bias in algorithmic filtering and personalization (2013, background): “‘‘helping user find items he cannot easily find himself’’ (Zhang and Hurley 2008) or ‘‘identifying a list of items that”
Diversity in Big Data: A Review (2017, unknown): “52 the diversiﬁcation problem is formulated as an optimization one under certain constraints.”
Temporal diversity in recommender systems (2010, background): “Zhang and Hurley [12] also focus on intra-list diversity, and optimize the trade oﬀ between users’ preferences and the diversity of the top-N results.”
Efficiency Improvement of Neutrality-Enhanced Recommendation (2013, background): “And Finally a system could provide tools for perspective-taking to see the world through other viewpoints.”