Diversity-Enforcing Item Recommendation Algorithm

Context:
- It can be implemented by a Diversity-Enforcing Data-Driven Item Recommendations System (to solve a diversity-enforcing data-driven item recommendations task).
Example(s):
Counter-Example(s):
- a Heuristic Item Recommendation Algorithm.
- a Data-Driven Spam Filtering Algorithm.
See: Collaborative Filtering Algorithm.

References

(Vargas & Castells, 2011) ⇒ Saúl Vargas, and Pablo Castells. (2011). “Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems.” In: Proceedings of the fifth ACM conference on Recommender systems, pp. 109-116 . ACM,
- ABSTRACT: The Recommender Systems community is paying increasing attention to novelty and diversity as key qualities beyond accuracy in real recommendation scenarios. Despite the raise of interest and work on the topic in recent years, we find that a clear common methodological and conceptual ground for the evaluation of these dimensions is still to be consolidated. Different evaluation metrics have been reported in the literature but the precise relation, distinction or equivalence between them has not been explicitly studied. Furthermore, the metrics reported so far miss important prop rties such as taking into consideration the ranking of recommended items, or whether items are relevant or not, when assessing the novelty and diversity of recommendations. We present a formal framework for the definition of novelty and diversity metrics that unifies and generalizes several state of the art metrics. We identify three essential ground concepts at the roots of novelty and diversity: choice, discovery and relevance, upon which the framework is built. …

(Zhou et al., 2010) ⇒ Tao Zhou, Zoltán Kuscsik, Jian-Guo Liu, Matúš Medo, Joseph Rushton Wakeling, and Yi-Cheng Zhang. (2010). “Solving the Apparent Diversity-accuracy Dilemma of Recommender Systems.” Proceedings of the National Academy of Sciences, 107(10). https://doi.org/10.1073/pnas.1000488107
- ABSTRACT: Recommender systems use data on past user preferences to predict possible future likes and interests. A key challenge is that while the most useful individual recommendations are to be found among diverse niche objects, the most reliably accurate results are obtained by methods that recommend objects based on user or object similarity. In this paper we introduce a new algorithm specifically to address the challenge of diversity and show how it can be used to resolve this apparent dilemma when combined in an elegant hybrid with an accuracy-focused algorithm. By tuning the hybrid appropriately we are able to obtain, without relying on any semantic or context-specific information, simultaneous gains in both accuracy and diversity of recommendations.

(Fleder & Hosanagar, 2009) ⇒ Daniel Fleder, and Kartik Hosanagar. (2009). “Blockbuster Culture's Next Rise Or Fall: The Impact of Recommender Systems on Sales Diversity.” Management science 55, no. 5