Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Automatic personalization based on Web usage mining
Communications of the ACM
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
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Collaborative filtering with decoupled models for preferences and ratings
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
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)
A graph model for E-commerce recommender systems
Journal of the American Society for Information Science and Technology
The multiple multiplicative factor model for collaborative filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
An MDP-Based Recommender System
The Journal of Machine Learning Research
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Expert Systems with Applications: An International Journal
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Electronic Commerce Research and Applications
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Increasing temporal diversity with purchase intervals
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Adaptive diversification of recommendation results via latent factor portfolio
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
App recommendation: a contest between satisfaction and temptation
Proceedings of the sixth ACM international conference on Web search and data mining
Opportunity model for e-commerce recommendation: right product; right time
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Is it time for a career switch?
Proceedings of the 22nd international conference on World Wide Web
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Traditional recommendation algorithms often select products with the highest predicted ratings to recommend. However, earlier research in economics and marketing indicates that a consumer usually makes purchase decision(s) based on the product's marginal net utility (i.e., the marginal utility minus the product price). Utility is defined as the satisfaction or pleasure user u gets when purchasing the corresponding product. A rational consumer chooses the product to purchase in order to maximize the total net utility. In contrast to the predicted rating, the marginal utility of a product depends on the user's purchase history and changes over time. According to the Law of Diminishing Marginal Utility, many products have the decreasing marginal utility with the increase of purchase count, such as cell phones, computers, and so on. Users are not likely to purchase the same or similar product again in a short time if they already purchased it before. On the other hand, some products, such as pet food, baby diapers, would be purchased again and again. To better match users' purchase decisions in the real world, this paper explores how to recommend products with the highest marginal net utility in e-commerce sites. Inspired by the Cobb-Douglas utility function in consumer behavior theory, we propose a novel utility-based recommendation framework. The framework can be utilized to revamp a family of existing recommendation algorithms. To demonstrate the idea, we use Singular Value Decomposition (SVD) as an example and revamp it with the framework. We evaluate the proposed algorithm on an e-commerce (shop.com) data set. The new algorithm significantly improves the base algorithm, largely due to its ability to recommend both products that are new to the user and products that the user is likely to re-purchase.