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CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
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The Journal of Machine Learning Research
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Improving personalized web search using result diversification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Personalized active learning for collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mean-Variance Analysis: A New Document Ranking Theory in Information Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Portfolio theory of information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
ACM Transactions on Computer-Human Interaction (TOCHI)
Temporal diversity in recommender systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
On statistical analysis and optimization of information retrieval effectiveness metrics
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent maximal marginal relevance
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Selectively diversifying web search results
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation
ACM Transactions on Internet Technology (TOIT)
The economics in interactive information retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Collaborative competitive filtering: learning recommender using context of user choice
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Intent-aware search result diversification
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Utilizing marginal net utility for recommendation in e-commerce
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Using control theory for stable and efficient recommender systems
Proceedings of the 21st international conference on World Wide Web
Dynamical information retrieval modelling: a portfolio-armed bandit machine approach
Proceedings of the 21st international conference companion on World Wide Web
Interactive collaborative filtering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
To personalize or not: a risk management perspective
Proceedings of the 7th ACM conference on Recommender systems
Set-oriented personalized ranking for diversified top-n recommendation
Proceedings of the 7th ACM conference on Recommender systems
Multi-objective mobile app recommendation: A system-level collaboration approach
Computers and Electrical Engineering
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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 (non-adpative), 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.