GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
PHOAKS: a system for sharing recommendations
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
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
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Automatic personalization based on Web usage mining
Communications of the ACM
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Comparison of Different Rating Based Collaborative Filtering Algorithms
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Proceedings of the ACM International Conference on Image and Video Retrieval
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Going beyond completeness in information retrieval
DNIS'07 Proceedings of the 5th international conference on Databases in networked information systems
Detecting relationships among categories using text classification
Journal of the American Society for Information Science and Technology
A hybrid recommendation method with reduced data for large-scale application
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Going beyond completeness in information retrieval
International Journal of Computational Science and Engineering
Rank order-based recommendation approach for multiple featured products
Expert Systems with Applications: An International Journal
Hybreed: A software framework for developing context-aware hybrid recommender systems
User Modeling and User-Adapted Interaction
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The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems--a personalized information filtering technology used to identify a set of N items that will be of interest to a certain user. User-based and model-based collaborative filtering are the most successful technology for building recommender systems to date and is extensively used in many commercial recommender systems. The basic assumption in these algorithms is that there are sufficient historical data for measuring similarity between products or users. However, this assumption does not hold in various application domains such as electronics retail, home shopping network, on-line retail where new products are introduced and existing products disappear from the catalog. Another such application domains is home improvement retail industry where a lot of products (such as window treatments, bathroom, kitchen or deck) are custom made. Each product is unique and there are very little duplicate products. In this domain, the probability of the same exact two products bought together is close to zero. In this paper, we discuss the challenges of providing recommendation in the domains where no sufficient historical data exist for measuring similarity between products or users. We present feature-based recommendation algorithms that overcome the limitations of the existing top-n recommendation algorithms. The experimental evaluation of the proposed algorithms in the real life data sets shows a great promise. The pilot project deploying the proposed feature-based recommendation algorithms in the on-line retail web site shows 75% increase in the recommendation revenue for the first 2 month period.