Book ChapterDOI
Context-Aware collaborative filtering system: predicting the user's preference in the ubiquitous computing environment
Annie Chen
- pp 244-253
TLDR
A context-aware collaborative filtering system that predicts a user's preference in different context situations based on past experiences and can help predict the user's behavior in different situations without the user actively defining it is presented.Abstract:
In this paper we present a context-aware collaborative filtering system that predicts a user's preference in different context situations based on past experiences. We extend collaborative filtering techniques so that what other like-minded users have done in similar context can be used to predict a user's preference towards an activity in the current context. Such a system can help predict the user's behavior in different situations without the user actively defining it. For example, it could recommend activities customized for Bob for the given weather, location, and traveling companion(s), based on what other people like Bob have done in similar context.read more
Citations
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Book
Mediation of User Models: for Enhanced Personalization in Recommender Systems
TL;DR: A generic user modeling data representation model is provided, which demonstrates its compatibility with existing recommendation techniques, and allows improving the quality of the recommendations provided to the users in certain conditions.
Journal ArticleDOI
A recommender system based on tag and time information for social tagging systems
Nan Zheng,Qiudan Li +1 more
TL;DR: The importance and usefulness of tag and time information when predicting users' preference and how to exploit such information to build an effective resource-recommendation model are investigated and a recommender system is designed to realize the computational approach.
Journal ArticleDOI
A Hidden Markov Model for Collaborative Filtering
TL;DR: A hidden Markov model is proposed to correctly interpret the users' product selection behaviors and make personalized recommendations and it is found that the proposed HMM-based collaborative filter performs as well as the best among the alternative algorithms when the data is sparse or static.
Journal ArticleDOI
A hidden Markov model for collaborative filtering
TL;DR: A hidden Markov model is proposed to correctly interpret the users' product selection behaviors and make personalized recommendations and it is found that the proposed HMM-based collaborative filter performs as well as the best among the alternative algorithms when the data is sparse or static.
Book ChapterDOI
Voting with your feet: an investigative study of the relationship between place visit behavior and preference
TL;DR: In this article, the authors investigated the relationship between explicit place ratings and implicit aspects of travel behavior such as visit frequency and travel time and found that when combined, visit frequency was correlated with place rating.
References
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Posted Content
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
TL;DR: In this article, the authors compare the predictive accuracy of various methods in a set of representative problem domains, including correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods.
Proceedings Article
Empirical analysis of predictive algorithms for collaborative filtering
TL;DR: Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.
Proceedings ArticleDOI
Explaining collaborative filtering recommendations
TL;DR: This paper presents experimental evidence that shows that providing explanations can improve the acceptance of ACF systems, and presents a model for explanations based on the user's conceptual model of the recommendation process.
Proceedings ArticleDOI
Recommender systems in e-commerce
TL;DR: An explanation of howRecommender systems help E-commerce sites increase sales, and a taxonomy of recommender systems, including the interfaces they present to customers, the technologies used to create the recommendations, and the inputs they need from customers.