Mining User Preferences, Page Content and Usage to Personalize Website Navigation

Flesca, Sergio; Greco, Sergio; Tagarelli, Andrea; Zumpano, Ester

doi:10.1007/s11280-005-1315-9

Cited by 35 publications

(21 citation statements)

References 27 publications

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“…Collaborative filtering recommender systems [8,25], based on user ratings, generate recommendations about movies, music or news. Personalize website navigation system [7] helps users navigate through potentially interesting pages easily. Web content filtering systems [2,9] assist users to retrieve related information from a large amount of data.…”

Section: Related Workmentioning

confidence: 99%

Opinion helpfulness prediction in the presence of “words of few mouths”

2011

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This paper identifies a widely existing phenomenon in social media content, which we call the "words of few mouths" phenomenon. This phenomenon challenges the development of recommender systems based on users' online opinions by presenting additional sources of uncertainty. In the context of predicting the "helpfulness" of a review document based on users' online votes on other reviews (where a user's vote on a review is either HELPFUL or UNHELPFUL), the "words of few mouths" phenomenon corresponds to the case where a large fraction of the reviews are each voted only by very few users. Focusing on the "review helpfulness prediction" problem, we illustrate the challenges associated with the "words of few mouths" phenomenon in the training of a review helpfulness predictor. We advocate probabilistic approaches for recommender system development in the presence of "words of few mouths". More concretely, we propose a probabilistic metric as the training target for conventional machine learning based predictors. Our empirical study using Support Vector Regression (SVR) augmented with the proposed probability metric demonstrates advantages of incorporating probabilistic methods in the training of the predictors. In addition to this "partially probabilistic" approach, we also develop a logistic regression based probabilistic model and correspondingly a learning algorithm for review helpfulness prediction. We demonstrate experimentally the superior performance of the logistic regression method over SVR, the prior art in review helpfulness prediction.

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Section: Related Workmentioning

confidence: 99%

Opinion helpfulness prediction in the presence of “words of few mouths”

2011

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“…Propagation has been used in many applications, e.g., static quality propagation [2,13,16,17,24,36], anchor text propagation [3], and relevance propagation [6,9,25,30,33]. Kleinberg proposed HITS algorithm for discovering hubs and authorities [16].…”

Section: Propagationmentioning

confidence: 99%

“…However, they also introduce more computations and destroy the concurrency properties of current propagation model. 9 http://www.citeulike.org/ 10 http://technorati.com/ The social annotations are usually modeled as a quad-tuple, i.e.<user, annotation, resource, time>which means that a user gives an annotation to a specific resource at a specific time.…”

Section: Propagation With More Constraintsmentioning

confidence: 99%

Social Propagation: Boosting Social Annotations for Web Mining

et al. 2009

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This paper is concerned with the problem of boosting social annotations using propagation, which is also called social propagation. In particular, we focus on propagating social annotations of web pages (e.g., annotations in Del.icio.us). Social annotations are novel resources and valuable in many web applications, including web search and browsing. Although they are developing fast, social annotations of web pages cover only a small proportion (<0.1%) of the World Wide Web. To alleviate the low coverage of annotations, a general propagation model based on Random Surfer is proposed. Specifically, four steps are included, namely basic propagation, multiple-annotation propagation, multiple-link-type propagation, and constraint-guided propagation. The model is evaluated on a dataset of 40,422 web pages randomly sampled from 100 most popular English sites and ten famous academic sites. Each page's annotations are obtained by querying the history interface of Del.icio.us. Experimental results show that the proposed model is very effective in increasing the coverage of annotations while still preserving novel properties of social annotations. Applications of propagated annotations on web search and classification further verify the effectiveness of the model.

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“…For example, photo and video sharing services (e.g., YouTube, Flickr) are causing an explosion of demand for multimedia content. These trends will determine a future mobile Web scenario characterized by a large amount of heterogeneous contents, mainly consisting of multimedia resources (e.g., [1], [2], [3]), that will have to be tailored to user preferences and to device capabilities on-thefly at the moment of the client request [4], [5], [6]. Designing server architectures that will support future Mobile Web-based services requires an initial evaluation of the workload and computational impact.…”

Section: Introductionmentioning

confidence: 99%

Resource Management Strategies for Mobile Web-Based Services

Canali

Colajanni

Lancellotti

2008

2008 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications

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Abstract-The great diffusion of Mobile Web-enabled devices allows the implementation of novel personalization, location and adaptation services that will place unprecedented strains on the server infrastructure of the content provider. This paper has a twofold contribution. First, we analyze the five-years trend of Mobile Web-based applications in terms of workload characteristics of the most popular services and their impact on the server infrastructures. As the technological improvements at the server level in the same period of time are insufficient to face the computational requirements of the future Mobile Web-based services, we propose and evaluate adequate resource management strategies. We demonstrate that pre-adaptating a small fraction of the most popular resources can reduce the response time up to one third thus facing the increased computational impact of the future Mobile Web services.

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Mining User Preferences, Page Content and Usage to Personalize Website Navigation

Cited by 35 publications

References 27 publications

Opinion helpfulness prediction in the presence of “words of few mouths”

Opinion helpfulness prediction in the presence of “words of few mouths”

Social Propagation: Boosting Social Annotations for Web Mining

Resource Management Strategies for Mobile Web-Based Services

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