By Olfa Nasraoui, Osmar Zaiane, Myra Spiliopoulou, Manshad Mobasher, Brij Masand, Philip Yu
This booklet constitutes the completely refereed post-proceedings of the seventh overseas Workshop on Mining internet facts, WEBKDD 2005, held in Chicago, IL, united states in August 2005 along side the eleventh ACM SIGKDD foreign convention on wisdom Discovery and knowledge Mining, KDD 2005. The 9 revised complete papers provided including an in depth preface went via rounds of reviewing and development and have been rigorously chosen for inclusion within the book.
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Extra resources for Advances in Web Mining and Web Usage Analysis: 7th International Workshop on Knowledge Discovery on the Web, WEBKDD 2005, Chicago, IL, USA, August 21,
Ga can be embedded in a transaction d ∈ D if there is an AP-frequent individual subgraph Gi whose abstraction is automorphic to Ga , and which can be embedded in d. 4 The fAP-IP Algorithm fAP-IP (“fast AP-IP miner”) consists of two graph-processing modules: (1) A standard frequent-subgraph miner operates on the transaction data abstracted according to the taxonomy. Following the apriori principle, this miner extends frequent patterns of size k − 1 by frequent patterns of size 1 (frequent edges) in order to generate the candidates of size k.
It shows that in this setting, time is sublinear in the number of patterns. Figure 1 (c) shows the combined eﬀects. It compares the mere addition of new transactions (data from (a)) with the addition of transactions with more patterns, and it shows that the algorithm still scales linearly with data set size. Similar graphs were obtained for a wide variety of parameter settings and for real data. Runtime increased with average pattern size (slightly more than linearly), but remained linear in dataset size.
Latency problem: Product catalogs evolve over time; however, the collaborative approach cannot deal with new products as they have not been previously rated. − Scalability. The computation of the neighborhood requires looking at all the items and users in the systems. Thus, the complexity grows with the number of users. − Privacy. In most systems, the similarity matrix is located on a server and is accessible to a third party, thus raising privacy concerns. − Shilling attacks: malicious users can alter user ratings in order to influence the recommendations.