Mining navigation history for recommendation

Fu, Xiaobin; Budzik, Jay; Hammond, Kristian J.

doi:10.1145/325737.325796

Cited by 158 publications

(100 citation statements)

References 5 publications

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“…Other approaches, such as [3,5,6,11], explicitly exploit user sessions and therefore are closer in spirit to our proposals. The SurfLen system [5] suggests interesting web pages to users based on the sets of URLs that are read together by many users and on the similarity between users (users that read a significant number of similar pages).…”

Section: Related Workmentioning

confidence: 91%

“…The SurfLen system [5] suggests interesting web pages to users based on the sets of URLs that are read together by many users and on the similarity between users (users that read a significant number of similar pages). The proposal described in [6] tackles the recommendation problem within a single e-commerce website and proposes an approach to recommend product pages (corresponding to product records in the website database) as well as other web pages (news about the company, product reviews, advises, etc.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, a huge amount of visitors is interested in exploring and analyzing the information published on them. As an example, the ec.europa.eu and europa.eu websites, managed by the European Commission, have been visited by more than 520M people in the last year 5 .…”

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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: 91%

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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show abstract

“…In the frame of web navigation [2], Sequential association rules (SAR) are used to capture dependences between resources. SAR are of the form X => Y where X (the antecedent) is a sequence of items.…”

Section: Sequential Association Rulesmentioning

confidence: 99%

History Dependent Recommender Systems Based on Partial Matching

Brun

Bonnin

Boyer

2009

User Modeling, Adaptation, and Personalization

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Abstract. This paper focuses on the utilization of the history of navigation within recommender systems. It aims at designing a collaborative recommender based on Markov models relying on partial matching in order to ensure high accuracy, coverage, robustness, low complexity while being anytime. Indeed, contrary to state of the art, this model does not simply match the context of the active user to the context of other users but partial matching is performed: the history of navigation is divided into several sub-histories on which matching is performed, allowing the matching constraints to be weakened. The resulting model leads to an improvement in terms of accuracy compared to state of the art models.

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“…In the case of web-based recommender systems (e.g. SurfLen [4], and Quickstep [9], among others) the usual approach involves taking into account the user's interests -either declared by the user or conjectured by the system-to rank or filter web pages. However such approaches differ from our proposal in that they do not attempt to perform a qualitative analysis to warrant recommendations.…”

Section: S Related Work Conclusionmentioning

confidence: 99%

Argument-based User Support Systems using Defeasible Logic Programming

Chesñevar

Maguitman

Simari

IFIP International Federation for Information Processing

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Abstract. Over the last few years, argumentation has been gaining increasing importance in several Al-related areas, mainly as a vehicle for facilitating rationally justifiable decision making when handling incomplete and potentially inconsistent information. In this setting, user support systems can rely on argumentation techniques to automatize reasoning and decision making in several situations such as the handling of complex policies or managing change in dynamic environments. This paper presents a generic argument-based approach to characterize user support systems, in which knowledge representation and inference are captured in terms of Defeasible Logic Progranmiing, a general-purpose defeasible argumentation formalism based on logic progranmung. We discuss a particular apphcation which has emerged as an instance of this approach oriented towards providing user decision support for web search.

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Mining navigation history for recommendation

Cited by 158 publications

References 5 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

History Dependent Recommender Systems Based on Partial Matching

Argument-based User Support Systems using Defeasible Logic Programming

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