Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Multidimensional Recommender Systems: A Data Warehousing Approach
WELCOM '01 Proceedings of the Second International Workshop on Electronic Commerce
Extrapolation methods for accelerating PageRank computations
WWW '03 Proceedings of the 12th international conference on World Wide Web
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Objectrank: authority-based keyword search in databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
FlexRecs: expressing and combining flexible recommendations
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
ItemRank: a random-walk based scoring algorithm for recommender engines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Serendipitous recommendations via innovators
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Training and testing of recommender systems on data missing not at random
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Ranking in context-aware recommender systems
Proceedings of the 20th international conference companion on World wide web
REQUEST: A Query Language for Customizing Recommendations
Information Systems Research
Flexible recommendation using random walks on implicit feedback graph
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
Random walk based entity ranking on graph for multidimensional recommendation
Proceedings of the fifth ACM conference on Recommender systems
Ranking objects by following paths in entity-relationship graphs
Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
PathRank: a novel node ranking measure on a heterogeneous graph for recommender systems
Proceedings of the 21st ACM international conference on Information and knowledge management
Combining prestige and relevance ranking for personalized recommendation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Hi-index | 0.00 |
As the volume of information on the Web is explosively growing, recommender systems have become essential tools for helping users to find what they need or prefer. Most existing systems are two-dimensional in that they only exploit User and Item dimensions and perform a typical form of recommendation 'Recommending Item to User'. Yet, in many applications, the capabilities of dealing with multidimensional information and of adapting to various forms of recommendation requests are very important. In this paper, we take a graph-based approach to accomplishing such requirements in recommender systems and present a generic graph-based multidimensional recommendation framework. Based on the framework, we propose two homogeneous graph-based and one heterogeneous graph-based multidimensional recommendation methods. We expect our approach will be useful for increasing recommendation performance and enabling flexibility of recommender systems so that they can incorporate various user intentions into their recommendation process. We present our research result that we have reached and discuss remaining challenges and future work.