Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying early buyers from purchase data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized recommendation driven by information flow
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Information flow modeling based on diffusion rate for prediction and ranking
Proceedings of the 16th international conference on World Wide Web
EigenRank: a ranking-oriented approach to collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
The structure of information pathways in a social communication network
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining social networks using heat diffusion processes for marketing candidates selection
Proceedings of the 17th ACM conference on Information and knowledge management
Journal of Artificial Intelligence Research
Determining user expertise for improving recommendation performance
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Recommend at opportune moments
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
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Different buyers exhibit different purchasing behaviors. Some rush to purchase new products while others tend to be more cautious, waiting for reviews from people they trust. In market analysis, the former group of buyers is often referred to as innovators and early adopters while the latter group is referred to as laggards. The adoption behavior is a dynamic feature of the user and varies over groups of products, e.g., innovators of literature may not be the innovators of electronics. The adoption order of users is a dynamic feature of the product, which can help to predict the future potential buyers. However, such dynamic features are usually unavailable in the description of products. In this paper, we study the user behavior of an online review website- Epinions.com. We first propose to model user adoption behaviors by creating a total ordering among users who rate the products in a given category. We develop a greedy algorithm and a Markov-chain based algorithm for computing the category total ordering. Next, we show that by using user behavior information, we can more accurately predict the category of a new product as well as predict which users will follow. Furthermore, by using the Epinion.com trust network as evidence, we demonstrate that our total ordering can group users into communities that closely resemble the trust network. Thus the adoption order can be a useful feature in recommendation systems.