Application of Spreading Activation Techniques in InformationRetrieval
Artificial Intelligence Review
ACM Transactions on Information Systems (TOIS)
A hybrid approach for searching in the semantic web
Proceedings of the 13th international conference on World Wide Web
Journal of Systems and Software
Impact of E-commerce on Consumers And Small Firms
Impact of E-commerce on Consumers And Small Firms
Pellet: A practical OWL-DL reasoner
Web Semantics: Science, Services and Agents on the World Wide Web
Automatic generation of concept hierarchies using WordNet
Expert Systems with Applications: An International Journal
Journal of Systems and Software
Spontaneous interaction with audiovisual contents for personalized e-commerce over Digital TV
Expert Systems with Applications: An International Journal
A personalized recommendation system based on product taxonomy for one-to-one marketing online
Expert Systems with Applications: An International Journal
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Ant colony optimization for RDF chain queries for decision support
Expert Systems with Applications: An International Journal
Prediction of members' return visit rates using a time factor
Electronic Commerce Research and Applications
Hi-index | 12.05 |
e-Commerce recommender systems select potentially interesting products for users by looking at their purchase histories and preferences. In order to compare the available products against those included in the user's profile, semantics-based recommendation strategies consider metadata annotations that describe their main attributes. Besides, to ensure successful suggestions of products, these strategies adapt the recommendations as the user's preferences evolve over time. Traditional approaches face two limitations related to the aforementioned features. First, product providers are not typically willing to take on the tedious task of annotating accurately a huge diversity of commercial items, thus leading to a substantial impoverishment of the personalization quality. Second, the adaptation process of the recommendations misses the time elapsed since the user has bought an item, which is an essential parameter that affects differently to each purchased product. This results in some pointless recommendations, e.g. including regularly items that the users are only willing to buy sporadically. In order to fight both limitations, we propose a personalized e-commerce system with two main features. On the one hand, we incentivize the users to provide high-quality metadata for commercial products; on the other, we explore a strategy that offers time-aware recommendations by combining semantic reasoning about these annotations with item-specific time functions. The synergetic effects derived from this combination lead to suggestions adapted to the particular needs of the users at any time. This approach has been experimentally validated with a set of users who accessed our personalized e-commerce system through a range of fixed and handheld consumer devices.