Abstract
Recommender systems are being used to help users find relevant items from a large set of alternatives in many online applications. Most existing recommendation techniques have focused on improving recommendation accuracy; however, diversity of recommendations has also been increasingly recognized in research literature as an important aspect of recommendation quality. This paper proposes a graph-theoretic approach for maximizing aggregate recommendation diversity based on maximum flow or maximum bipartite matching computations. The proposed approach is evaluated using real-world movie rating datasets and demonstrates substantial improvements in both diversity and accuracy, as compared to the recommendation re-ranking approaches, which have been introduced in prior literature for the purpose of diversity improvement.
Original language | English (US) |
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Pages (from-to) | 3-10 |
Number of pages | 8 |
Journal | CEUR Workshop Proceedings |
Volume | 816 |
State | Published - 2011 |
Event | Workshop on Novelty and Diversity in Recommender Systems, DiveRS 2011 - At the 5th ACM International Conference on Recommender Systems, RecSys 2011 - Chicago, IL, United States Duration: Oct 23 2011 → Oct 23 2011 |
Keywords
- Aggregate diversity
- Collaborative filtering
- Graph-based algorithms
- Recommendation diversity