Improving sales diversity by recommending users to items

Saúl Vargas, Pablo Castells
2014 Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14  
Sales diversity is considered a key feature of Recommender Systems from a business perspective. Sales diversity is also linked with the long-tail novelty of recommendations, a quality dimension from the user perspective. We explore the inversion of the recommendation task as a means to enhance sales diversity -and indirectly novelty -by selecting which users an item should be recommended to instead of the other way around. We address the inverted task by two approaches: a) inverting the rating
more » ... atrix, and b) defining a probabilistic reformulation which isolates the popularity component of arbitrary recommendation algorithms. We find that the first approach gives rise to interesting reformulations of nearest-neighbor algorithms, which essentially introduce a new neighbor selection policy. The second approach, as well as the first, ultimately result in substantial sales diversity enhancements, and improved trade-offs with recommendation precision and novelty. Two experiments on movie and music recommendation datasets show the effectiveness of the resulting approach, even when compared to direct optimization approaches of the target metrics proposed in prior work.
doi:10.1145/2645710.2645744 dblp:conf/recsys/VargasC14 fatcat:pd5snrotnvehtg7jehmjkcvkxi