Automatic personalization based on Web usage mining

Mobasher, Bamshad; Cooley, Robert; Srivastava, Jaideep

doi:10.1145/345124.345169

Cited by 910 publications

(476 citation statements)

References 6 publications

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“…There are also some proposals that generalize the problem of web page recommendation to that of web personalization (e.g., see [4,8]). The goal of web personalization is rather to compute a collection of relevant objects of different types to recommend [8], such as URLs, ads, texts, and products, and compose customized web pages.…”

Section: Related Workmentioning

confidence: 99%

“…The goal of web personalization is rather to compute a collection of relevant objects of different types to recommend [8], such as URLs, ads, texts, and products, and compose customized web pages. So, a website can be personalized by adapting its content and/or structure: adding new links, highlighting existing links, or creating new pages [4].…”

Section: Related Workmentioning

confidence: 99%

“…The "Comune di Modena" Town Hall website 8 has been used in our experiments. This is the official website of an Italian city, having a population of about 200000 citizens.…”

Section: Datasetmentioning

confidence: 99%

See 2 more Smart Citations

Exploiting Linguistic Analysis on URLs for Recommending Web Pages: A Comparative Study

Cadegnani

Guerra

Ilarri

et al. 2017

Transactions on Computational Collective Intelligence XXVI

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Abstract. Institutional websites of governments, public bodies and many other organizations must publish and keep updated a large amount of information stored in thousands of web pages in order to satisfy the demands of citizens. Nowadays, citizens require high levels of quality information from public institutions in order to guarantee their transparency. In this scenario, the "search form", which is typically available in websites, does not adequately support the users, since it requires the users to explicitly express their information needs through keywords. The so-called "long tail" phenomenon, typical observed in e-commerce portals, also affects this kind of websites: not all the pages can be adequately displayed with a high level of importance, and so users with no popular requests can spend a lot of time to locate the information they need. Therefore, users need support for improving the effectiveness of their navigation. In these scenarios, the development of a recommending system predicting the next page a user wants to see in a large website has gained importance. For addressing this issue, complex models and approaches for recommending web pages that usually require to process personal user preferences have been proposed.In this paper, we analyze and compare three different approaches to leverage information embedded in the structure of web sites and the logs of their web servers to improve the effectiveness of web page recommendation. Our proposals exploit the context of the users' navigations, i.e., their current sessions when surfing a specific web site. These approaches do not require either information about the personal preferences of the users to be stored and processed, or complex structures to be created and maintained. So, they can be easily incorporated to current large websites to facilitate the users' navigation experience. Finally, the paper reports some comparative experiments using a real-world website to analyze the performance of the proposed approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Exploiting Linguistic Analysis on URLs for Recommending Web Pages: A Comparative Study

Cadegnani

Guerra

Ilarri

et al. 2017

Transactions on Computational Collective Intelligence XXVI

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“…Such systems take as input a set of rules of the form precondition  action, as well as a set of data and produce their recommendations in three phases [24]:…”

Section: Recommendation Servicementioning

confidence: 99%

Challenges in Context-Aware Mobile Language Learning: The MASELTOV Approach

Gaved

Luley

Efremidis

et al. 2014

Mobile as a Mainstream – Towards Future Challenges in Mobile Learning

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Abstract. Smartphones, as highly portable networked computing devices with embedded sensors including GPS receivers, are ideal platforms to support context-aware language learning. They can enable learning when the user is engaged in everyday activities while out and about, complementing formal language classes. A significant challenge, however, has been the practical implementation of services that can accurately identify and make use of context, particularly location, to offer meaningful language learning recommendations to users. In this paper we review a range of approaches to identifying context to support mobile language learning. We consider how dynamically changing aspects of context may influence the quality of recommendations presented to a user. We introduce the MASELTOV project's use of context awareness combined with a rules-based recommendation engine to present suitable learning content to recent immigrants in urban areas; a group that may benefit from contextual support and can use the city as a learning environment.

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“…In particular, web log mining is a commonly used approach of analyzing web log data with data mining techniques for the discovery of sequential patterns, association rules or user clusters. Such mining techniques have been applied to provide personalized link recommendations to web users [10]. Thereby the user profile of the current user is matched against one or more previously discovered usage profiles.…”

Section: Related Workmentioning

confidence: 99%

Preference Mining: A Novel Approach on Mining User Preferences for Personalized Applications

Holland

Ester

Kießling

2003

Knowledge Discovery in Databases: PKDD 2003

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Abstract.Advanced personalized e-applications require comprehensive knowledge about their user's likes and dislikes in order to provide individual product recommendations, personal customer advice and custom-tailored product offers. In our approach we model such preferences as strict partial orders with "A is better than B" semantics, which has been proven to be very suitable in various e-applications. In this paper we present novel Preference Mining techniques for detecting strict partial order preferences in user log data. The main advantage of our approach is the semantic expressiveness of the Preference Mining results. Experimental evaluations prove the effectiveness and efficiency of our algorithms. Since the Preference Mining implementation uses sophisticated SQL statements to execute all data-intensive operations on database layer, our algorithms scale well even for large log data sets. With our approach personalized e-applications can gain valuable knowledge about their customers' preferences, which is essential for a qualified customer service.

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Automatic personalization based on Web usage mining

Cited by 910 publications

References 6 publications

Exploiting Linguistic Analysis on URLs for Recommending Web Pages: A Comparative Study

Exploiting Linguistic Analysis on URLs for Recommending Web Pages: A Comparative Study

Challenges in Context-Aware Mobile Language Learning: The MASELTOV Approach

Preference Mining: A Novel Approach on Mining User Preferences for Personalized Applications

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