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178 8 Web Mining and Recommendation SystemsTime-complexityThe time-complexity of the basic k-MEANS is reported to be O(n) that can be seenin [122]; since the similarity ordistance between the data points and centroids as aconstant in the basic one, the time-complexity in Hybrid approach becomes O(mn)because that cost is O(m). So the offline time-complexity of Hybrid approach isO(mn).During the online stage, O(k) similarity weight calculations are needed forthe target user, each of which takes O(m)times; so, the online time-complexity isO(km) ≃ O(m)8.2.3 Experimental ObservationsIn [212], the authors gave the evaluation results of recommendation. The two datasetsi.e. ML1N and MLCURRENT used in the experiments are from the same data sourcedescribed in Sec.8.1. Here we select a few conclusive results presented. Figure 8.5plots the predictive performance of Hybrid approach - CLUSTKNN both for the metricsNMAE and EU used in [212], and for both of the datasets. Since such Hybridapproach can be regarded as approximating user-based KNN with the two becomingequivalent when k equals the number of user in the system, we have also includedthe predictive performance of user-based KNN in the plots a line as the upper boundfor such Hybrid approach. It is seen that the hybrid approach of user-based and itembasedalgorithm consistently outperforms the classic user-based algorithm alone.8.3 User Profiling for Web Recommendation Based on PLSA andLDA ModelIn Chap.6, we have discussed using latent semantic analysis for Web usage mining.The results of Web usage mining are the user profiles that are in the forms ofuser session clusters. In this section, we will describe how to utilize the user profilesfor Web recommendation based on PLSA and LDA model, respectively. In previoussections, we have derived user access patterns and user profiles via a probability inferencealgorithm. In the following parts, we aim to incorporate the discovered usageknowledge with the collaborative filtering algorithm into the Web recommendationalgorithm.8.3.1 Recommendation Algorithm based on PLSA ModelAs discussed in previous sections, Web usage mining will result in a set of usersession clusters SCL = {SCL 1 ,SCL 2 ,···SCL k } , where each SCL i is a collection ofuser sessions with similar access preference. And from the discovered user sessionclusters, we can then generate their corresponding centroids of the user session clusters,which are considered as usage profiles, or user access patterns. The completeformulation of usage profiling algorithm is expressed as follows:
8.3 User Profiling for Web Recommendation Based on PLSA and LDA Model 179(a)(b)(c)(d)Fig. 8.5. Prediction performance of Hybrid approach:(a)-(b) on ML1M , and (c)-(d) onMLCURRENT dataset. Results of user-based KNN are shown for comparison.
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Web Mining and Social Networking
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Guandong Xu • Yanchun Zhang • L
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VIIIPrefacefollowing characteristic
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Acknowledgements: We would like to
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XIVContents3.1.2 Basic Algorithms f
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XVIContentsPart III Social Networki
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Part IFoundation
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4 1 Introduction(3). Learning usefu
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6 1 Introductioncalled computationa
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8 1 Introduction• The data on the
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10 1 Introductionin a broad range t
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2Theoretical BackgroundsAs discusse
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2.2 Textual, Linkage and Usage Expr
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2.4 Eigenvector, Principal Eigenvec
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2.5 Singular Value Decomposition (S
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2.6 Tensor Expression and Decomposi
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2.7 Information Retrieval Performan
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2.8 Basic Concepts in Social Networ
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2.8 Basic Concepts in Social Networ
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4Web Content MiningIn recent years
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score(q,d)=4.2 Web Search 73V(q) ·
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4.2 Web Search 75algorithm. The Web
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4.3 Feature Enrichment of Short Tex
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4.4 Latent Semantic Indexing 794.4
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Notation4.5 Automatic Topic Extract
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4.5 Automatic Topic Extraction from
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4.6 Opinion Search and Opinion Spam
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4.6 Opinion Search and Opinion Spam
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90 5 Web Linkage Mining5.2 Co-citat
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104 5 Web Linkage MiningFig. 5.10.
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106 5 Web Linkage Miningbetween use
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6Web Usage MiningIn previous chapte
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6.1 Modeling Web User Interests usi
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6.2 Web Usage Mining using Probabil
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6.3 Finding User Access Pattern via
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