The application of web mining to personalization has a long
tradition in electronic commerce research. In this empirical
study we focus speci cally on mining sequential navigation
patterns from weblogs and thoroughly compare di erent
design variants for making personalized suggestions to
users. In particular we concentrate on the impact of additional
product knowledge like item characteristics, di erent
properties of the sequential pattern mining process such as
closure as well as rule quality metrics such as support, con-
dence and lift, and evaluate the recommender's accuracy
by experimenting on historical web sessions.
This paper therefore rstly demonstrates how state of the art
sequence mining algorithms such as Pre xSpan and BIDE
may be adapted to the speci c problem of extracting sequential
rules from e-commerce weblogs. Furthermore, in
order to compact the resulting rule set, the -closed criteria
is proposed as a logical extention to closed and maximal
frequent patterns to eliminate spurious sequences. Finally,
our experimental ndings show that using multidimensional
sequential patterns and the lift metric for weighting personalization
rules can boost recall to 28% of all actual purchase
transactions when using only short navigational sequences.