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Adaptive diversification of recommendation results via latent factor portfolio.
| Content Provider | CiteSeerX |
|---|---|
| Author | Shia, Yue Zhaob, Xiaoxue Wangb, Jun Larsona, Martha Alan Hanjalica, A. |
| Abstract | This paper studies result diversification in collaborative filtering. We argue that the diversification level in a recommendation list should be adapted to the target users ’ individual situations and needs. Different users may have different ranges of interests – the preference of a highly focused user might include only few topics, whereas that of the user with broad interests may encompass a wide range of topics. Thus, the recommended items should be diversified according to the interest range of the target user. Such an adaptation is also required due to the fact that the uncertainty of the estimated user preference model may vary significantly between users. To reduce the risk of the recommendation, we should take the difference of the uncertainty into account as well. In this paper, we study the adaptive diversification problem theoretically. We start with commonly used latent factor models and reformulate them using the mean-variance analysis from the portfolio theory in text retrieval. The resulting Latent Factor Portfolio (LFP) model captures the user’s interest range and the uncertainty of the user preference by employing the variance of the learned user latent factors. It is shown that the correlations between items (and thus the item diversity) can be obtained by using the correlations between latent factors (topical diversity), which in return significantly reduce the computation load. Our mathematical derivation also reveals that diversification is necessary, not only for risk-averse system behavior (nonadpative), but also for the target users ’ individual situations (adaptive), which are represented by the distribution and the variance of the latent user factors. Our experiments confirm the theoretical insights and show that LFP succeeds in improving latent factor models by adaptively introducing recommendation diversity to fit the individual user’s needs. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Latent Factor Portfolio Adaptive Diversification Recommendation Result Target User Individual Situation Latent Factor Model Wide Range Interest Range Recommended Item Latent User Factor Portfolio Theory Broad Interest Risk-averse System Behavior User Interest Range Paper Study Theoretical Insight Topical Diversity Computation Load Recommendation List Target User Recommendation Diversity Item Diversity Mathematical Derivation Latent Factor Different User Mean-variance Analysis User Latent Factor Diversification Level Collaborative Filtering Different Range Adaptive Diversification Problem Text Retrieval Individual User Need User Preference Model User Preference |
| Content Type | Text |
| Resource Type | Article |
