Behavior Modeling using Bigram Frequencies for Client-Side Link Prefetching

A. Georgakis, H. Li (Sweden), and M. Gordan (Romania)


Pre-fetching, user behaviour modelling, bigrams


The perceived latency for a user surfing the Internet is the target of a transparent and speculative algorithm that relies on a user behavior model. The model is based on past user behavior and in combination with a weighting scheme for the outbound links of a particu lar web page, aims at reducing the perceived latencies. The assistance is in the form of prefetching some linked web pages and storing them in the browser’s cache. A comparison between the proposed algorithms against two other prefetching algorithms yield improved cache hit rates given a moderate bandwidth overhead. Fur thermore, the experimental results are proven to be statistically significant.

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