Name
Abstract | ||
|---|---|---|
In this paper, we derive a probabilistic ranking framework for diversifying the recommendations of baseline methods. Unlike conventional approaches to balance relevance and diversity, we produce the diversified list by maximizing user’s current marginal aspect preference, thus avoiding the hyperparameters in making the tradeoff. Before diversification, we adopt clustering to generate a much smaller set of candidate items based on three requirements: efficiency, relevance and diversity. As a result, it helps us not only reduce the search space greatly but also promote a slight increase in performance. Our framework is flexible to incorporate new preference aspects and apply new marginal aspect preference algorithms. Evaluation results show that our method can get better diversity than others and maintain comparable accuracy to baseline methods, thus a better balance between relevance and diversity. © Springer International Publishing Switzerland 2015. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-25255-1_55 | APWeb |
Keywords | Field | DocType |
recommendation diversification, framework, candidate generation, marginal preference | Data mining,Ranking,Hyperparameter,Computer science,Diversification (marketing strategy),Probabilistic logic,Cluster analysis,Probabilistic framework | Conference |
Volume | ISSN | Citations |
9313 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Yang-Hui | 1 | 0 | 0.68 |
| Zhou Ying-Min | 2 | 0 | 0.68 |
| Zheng Hai-Tao | 3 | 142 | 24.39 |