Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Capturing knowledge of user preferences: ontologies in recommender systems
Proceedings of the 1st international conference on Knowledge capture
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
IEEE Transactions on Knowledge and Data Engineering
An Ontology-Based Product Recommender System for B2B Marketplaces
International Journal of Electronic Commerce
OOPS: User Modeling Method for Task Oriented Mobile Internet Services
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Automatically building research reading lists
Proceedings of the fourth ACM conference on Recommender systems
Content-based recommendation in social tagging systems
Proceedings of the fourth ACM conference on Recommender systems
The intention behind web queries
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
Construction and use of role-ontology for task-based service navigation system
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Intuitive Topic Discovery by Incorporating Word-Pair's Connection Into LDA
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Automatic task-based profile representation for content-based recommendation
International Journal of Knowledge-based and Intelligent Engineering Systems
| Hi-index | 0.00 |
This paper proposes a content-based recommendation method tuned for multiple content domains. Most content-based recommender systems use content attributes (e.g. description of content and category of content) to represent items and user profiles. This raises the problem that when recommending content from new content domain, the recommendation quality will be poor until the item profile representation is updated through the input of a sufficient quantity of contents in the new content domain. In this paper, to develop an item profile representation that is independent of the contents, we propose a method that uses common categories to represent item profiles; it focuses on the real world activities (tasks) that attract the user's interest. Concretely, we present a light-weight task-model that covers a wide variety of tasks and yields item profiles. We also create a recommendation algorithm by incorporating the proposed profile representation into a statistical SVM(Support Vector Machine) based recommendation algorithm. Finally, we conduct a user test and the results of this test show 9% higher user evaluation scores of content recommendations compared to an existing content-based recommendation algorithm using term-based profile representation. This shows the effectiveness of our user-centered profile representation approach.