Abstract
In recent years, clickstream-based Web personalization models for collaborative filtering recommendation have received much attention mainly due to their scalability [10,16,19]. The common personalization models are the Markov model, (sequential) association rule, and clustering. These models have shown strengths and weaknesses in their performance: for instance, the Markov model has higher precision and lower recall than (sequential) association rule and clustering, and vice versa [22]. In order to address the trade-off relationship of precision and recall, some study has combined two or more different models [22] or applied multi-order models [24,27]. The performance increases by these models, however, are at best marginal and still there is room for improving the performance because of their first order (one model type) application in making recommendation. We propose a new hybrid model for improving the performance, especially recall. The proposed hybrid model applies four prediction models - the Markov model, sequential association rule, association rule, and a default model [1,17] - in tandem in their precision order. We evaluated our model with Web usage data, and the result is promising.
Original language | American English |
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Pages | 88-95 |
Number of pages | 8 |
State | Published - 2004 |
Event | WIDM 2004: Proceedings of the Sixth ACM International Workshop on Web Information and Data Management - Washington, DC, United States Duration: Nov 12 2004 → Nov 13 2004 |
Other
Other | WIDM 2004: Proceedings of the Sixth ACM International Workshop on Web Information and Data Management |
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Country/Territory | United States |
City | Washington, DC |
Period | 11/12/04 → 11/13/04 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
Keywords
- Association rule
- Clickstream
- Clustering
- Collaborative filtering recommendation
- Default model
- F measure
- Hybrid model
- Markov model
- Performance
- Personalization model
- Precision
- Recall
- Sequential association rule