Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Comparing Rank and Score Combination Methods for Data Fusion in Information Retrieval
Information Retrieval
IEEE Transactions on Knowledge and Data Engineering
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Recommender systems and their impact on sales diversity
Proceedings of the 8th ACM conference on Electronic commerce
Effective missing data prediction for collaborative filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
EigenRank: a ranking-oriented approach to collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
BrowseRank: letting web users vote for page importance
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Search Engines: Information Retrieval in Practice
Search Engines: Information Retrieval in Practice
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Fast nonparametric matrix factorization for large-scale collaborative filtering
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A social recommendation framework based on multi-scale continuous conditional random fields
Proceedings of the 18th ACM conference on Information and knowledge management
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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The research issue of recommender systems has been treated as a classical regression problem over the decades and has obtained a great success. In the next generation of recommender systems, multi-criteria recommendation has been predicted as an important direction. Different from traditional recommender systems that aim particularly at recommending high-quality items evaluated by users' ratings, inmulti-criteria recommendation, quality only serves as one criterion, and many other criteria such as relevance, coverage, and diversity should be simultaneously optimized. Although recently there is work investigating each single criterion, there is rarely any literature that reports how each single criterion impacts each other and how to combine them in real applications. Thus in this paper, we study the relationship of two criteria, quality and relevance, as a preliminary work in multi-criteria recommendation. We first give qualitative and quantitative analysis of competitive quality-based and relevance-based algorithms in these two criteria to show that both algorithms cannot work well in the opposite criteria. Then we propose an integrated metric and finally investigate how to combine previous work together into an unified model. In the combination, we introduce a Continuous-time MArkov Process (CMAP) algorithm for ranking, which enables principled and natural integration with features derived from both quality-based and relevance-based algorithms. Through experimental verification, the combined methods can significantly outperform either single quality-based or relevance-based algorithms in the integrated metric and the CMAP model outperforms traditional combination methods by around 3%. Its linear complexity with respect to the number of users and items leads to satisfactory performance, as demonstrated by the around 7-hour computational time for over 480k users and almost 20k items.