Recommender systems and their impact on sales diversity

By Daniel M. Fleder, Kartik Hosanagar. 2007

188 citations recorded by the source corpus.

Subfields: Information Systems, Marketing

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. Tomaz Pozrl
2. Matevz Kunaver
3. Azzurra Ragone
4. Vito Bellini
5. Angelo Schiavone
6. Eugenio Di Sciascio
7. Hongzhi Yin
8. Tommaso Di Noia

In Marketing

1. Tomaz Pozrl
2. Matevz Kunaver
3. Angelo Schiavone
4. Bo Long
5. Bin Cui
6. Hongyuan Zha
7. Tommaso Di Noia
8. Eugenio Di Sciascio

Citations in the corpus

• How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, background): “forts have been made to understand whether or not various recommender systems impact diversity by reinforcing the popularity of already-popular products or by increasing interactions with niche items [2, 11, 15, 19, 20, 46, 48, 50, 62, 67, 68]. These systems also have the potential to create “echo-c”
• Computing Recommendations via a Knowledge Graph-aware Autoencoder (2018, methodology): “Then, recommendation lists are compared with the test set by computing both performance and diversity metrics such as Precision, Recall, F-1 score, nDCG [12], aggregate diversity, and Gini index as a measure of sales diversity [8].”
• Collaborative competitive filtering: learning recommender using context of user choice (2011, background): “Indeed, this data sparseness issue has been widely recognized as a key challenge for constructing effective recommender systems.”
• Challenging the Long Tail Recommendation (2012, methodology): “• Diversity measurement: Authors in [7] examine the effect of recommender systems on the diversity of sales, and they arrive at a surprising conclusion that most of existing recommenders can lead to reduction in sales diversity.”
• Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity (2009, background): “That diversity can decrease is surprising to consumers who express that recommendations have helped them discover new products.”
• Diversity in recommender systems - A survey (2017, methodology): “This was followed up in 2007 by Fleder and Hosanagar [41] who performed an experiment that showed that most of RSs reduce the diversity of recommended items by focusing on the accuracy recommendations for each user.”
• Recommender Systems (2016, unknown): “The impact of recommender systems on sales diversity is discussed in [203].”