Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
RecTree: An Efficient Collaborative Filtering Method
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on 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)
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative Filtering for Multi-class Data Using Belief Nets Algorithms
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
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 the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Factorizing personalized Markov chains for next-basket recommendation
Proceedings of the 19th international conference on World wide web
Temporal diversity in recommender systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Temporal recommendation on graphs via long- and short-term preference fusion
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards a theory model for product search
Proceedings of the 20th international conference on World wide web
Utilizing marginal net utility for recommendation in e-commerce
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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
Community-based user recommendation in uni-directional social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The development of Web 2.0 technology has led to huge economic benefits and challenges for both e-commerce websites and online shoppers. One core technology to increase sales and consumers' satisfaction is the use of recommender systems. Existing product recommender systems consider the order of items purchased by users to obtain a list of recommended items. However, they do not consider the time interval between the products purchased. For example, there is often an interval of 2-3 months between the purchase of printer ink cartridges or refills. Thus, recommending appropriate ink cartridges one week before the user needs to replace the depleted ink cartridges would increase the likelihood of a purchase decision. In this paper, we propose to utilize the purchase interval information to improve the performance of the recommender systems for e-commerce. We design an efficient algorithm to compute the purchase intervals between product pairs from users' purchase history and integrate this information into the marginal utility model. We evaluate our approach on a real world ecommerce dataset. Experimental results demonstrate that our approach significantly improves the conversion rate and temporal diversity compared to state-of-the-art algorithms.