Abstract
Recent research in mining user access patterns for predicting Web page requests focuses only on consecutive sequential Web page accesses, i.e., pages which are accessed by following the hyperlinks. In this paper, we propose a new method for mining user access patterns that allows the prediction of multiple non-consecutive Web pages, i.e., any pages within the Web site. Our approach consists of two major steps. First, the shortest path algorithm in graph theory is applied to find the distances between Web pages. In order to capture user access behavior on the Web, the distances are derived from user access sequences, as opposed to static structural hyperlinks. We refer to these distances as Minimum Reaching Distance (MRD) information. The association rule mining (ARM) technique is then applied to form a set of predictive rules which are further refined and pruned by using the MRD information. The proposed approach is applied as a collaborative filtering technique to recommend Web pages within a Web site. Experimental results demonstrate that our approach improves performance over the existing Markov model approach in terms of precision and recall, and also has a better potential of reducing the user access time on the Web.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - International Workshop on Challenges in Web Information Retrieval and Integration, WIRI'05 |
| Pages | 128-133 |
| Number of pages | 6 |
| Volume | 2005 |
| DOIs | |
| State | Published - Dec 1 2005 |
| Event | International Workshop on Challenges in Web Information Retrieval and Integration, WIRI'05 - Tokyo, Japan Duration: Apr 8 2005 → Apr 9 2005 |
Other
| Other | International Workshop on Challenges in Web Information Retrieval and Integration, WIRI'05 |
|---|---|
| Country | Japan |
| City | Tokyo |
| Period | 4/8/05 → 4/9/05 |
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Keywords
- Association rule mining
- Collaborative filtering
- Web data extraction
- Web log/navigation path analysis
ASJC Scopus subject areas
- Engineering(all)
Cite this
Collaborative filtering by mining association rules from user access sequences. / Shyu, Mei-Ling; Haruechaiyasak, Choochart; Chen, Shu Ching; Zhao, Na.
Proceedings - International Workshop on Challenges in Web Information Retrieval and Integration, WIRI'05. Vol. 2005 2005. p. 128-133 1553005.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Collaborative filtering by mining association rules from user access sequences
AU - Shyu, Mei-Ling
AU - Haruechaiyasak, Choochart
AU - Chen, Shu Ching
AU - Zhao, Na
PY - 2005/12/1
Y1 - 2005/12/1
N2 - Recent research in mining user access patterns for predicting Web page requests focuses only on consecutive sequential Web page accesses, i.e., pages which are accessed by following the hyperlinks. In this paper, we propose a new method for mining user access patterns that allows the prediction of multiple non-consecutive Web pages, i.e., any pages within the Web site. Our approach consists of two major steps. First, the shortest path algorithm in graph theory is applied to find the distances between Web pages. In order to capture user access behavior on the Web, the distances are derived from user access sequences, as opposed to static structural hyperlinks. We refer to these distances as Minimum Reaching Distance (MRD) information. The association rule mining (ARM) technique is then applied to form a set of predictive rules which are further refined and pruned by using the MRD information. The proposed approach is applied as a collaborative filtering technique to recommend Web pages within a Web site. Experimental results demonstrate that our approach improves performance over the existing Markov model approach in terms of precision and recall, and also has a better potential of reducing the user access time on the Web.
AB - Recent research in mining user access patterns for predicting Web page requests focuses only on consecutive sequential Web page accesses, i.e., pages which are accessed by following the hyperlinks. In this paper, we propose a new method for mining user access patterns that allows the prediction of multiple non-consecutive Web pages, i.e., any pages within the Web site. Our approach consists of two major steps. First, the shortest path algorithm in graph theory is applied to find the distances between Web pages. In order to capture user access behavior on the Web, the distances are derived from user access sequences, as opposed to static structural hyperlinks. We refer to these distances as Minimum Reaching Distance (MRD) information. The association rule mining (ARM) technique is then applied to form a set of predictive rules which are further refined and pruned by using the MRD information. The proposed approach is applied as a collaborative filtering technique to recommend Web pages within a Web site. Experimental results demonstrate that our approach improves performance over the existing Markov model approach in terms of precision and recall, and also has a better potential of reducing the user access time on the Web.
KW - Association rule mining
KW - Collaborative filtering
KW - Web data extraction
KW - Web log/navigation path analysis
UR - http://www.scopus.com/inward/record.url?scp=33845357693&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33845357693&partnerID=8YFLogxK
U2 - 10.1109/WIRI.2005.14
DO - 10.1109/WIRI.2005.14
M3 - Conference contribution
SN - 0769524141
SN - 9780769524146
VL - 2005
SP - 128
EP - 133
BT - Proceedings - International Workshop on Challenges in Web Information Retrieval and Integration, WIRI'05
ER -