1,720,988 research outputs found
Categorization of Web users by fuzzy clustering
No full textCategorization of users is a fundamental task inWeb personalization. Fuzzy clustering is a valid approach to derive user categories by capturing similar user interests from web usage data available in log files. Usually, fuzzy clustering is based on the use of Euclidean metrics to evaluate similarity between user preferences. This can lead to user categories that do not capture the semantic information incorporated in the original Web usage data. To better capture similarity between users, in this paper we propose the use of a measure that is based on the evaluation of similarity between fuzzy sets. The proposed fuzzy measure is employed in a relational fuzzy clustering algorithm to discover clusters embedded in the Web usage data and derive categories modeling the preferences of similar users. An application example on usage data extracted from log files of a real Web site is reported and a comparison with
the results obtained using the cosine measure is shown to demonstrate the effectiveness of the fuzzy similarity measure
Newer: a system for neuro-fuzzy web recommendation
No full textIn the era of the Web, there is urgent need for developing systems able to personalize the online experience of Web users on the basis of their needs. Web recommendation is a promising technology that attempts to predict the interests of Web users, by providing them with information and/or services that they need without explicitly asking for them. In this paper we propose NEWER, a usage-based Web recommendation system that exploits the potential of Computational Intelligence techniques to dynamically suggest interesting pages to users according to their preferences. NEWER employs a neuro-fuzzy approach in order to determine categories of users sharing similar interests and to discover a recommendation model as a set of fuzzy rules expressing the associations between user categories and relevances of pages. The discovered model is used by a online recommendation module to determine the list of links judged relevant for users. The results obtained on both synthetic and real-world data show that NEWER is effective for recommendation, leading to a quality of the generated recommendations comparable and often significantly better than those of other approaches employed for the comparison
Dynamic link suggestion by a neuro-fuzzy web recommendation system
No full textIn this paper we explore the use of a neuro-fuzzy strategy to develop a Web personalization system that
dynamically suggests interesting URLs for the current user according to a collaborative filtering approach.
As a preliminary step, user access logs are analyzed to identify user sessions. Then, groups of users which
exhibit a common browser behavior (i.e. user profiles) are discovered by applying a fuzzy clustering
algorithm to the user sessions. Finally, a hybrid approach based on the combination of the fuzzy reasoning
and the connectionist paradigm is applied in order to derive fuzzy associations between user profiles and
relevant Web pages to be suggested to users. The derived knowledge is ultimately used by an online
recommendation module to dynamically suggest links to Web pages judged interesting for the current user.
Some preliminary experimental results are presented on a real life web log dataset
A neuro-fuzzy collaborative filtering approach for Web recommendation
No full textDue to the growing variety and quantity of information available on the Web, there is urgent need for developing web-based applications capable of adapting their services to the needs of the users. This is the main rationale behind the flourishing area of Web recommendation, that finds in Soft Computing techniques a valid tool to handle uncertainty in web usage data and develop web-based applications tailored on users preferences. In this context, we propose a neuro-fuzzy strategy that combines soft computing techniques to develop a Web recommendation system that dynamically suggests interesting URLs for the current user. As a preliminary step, user access logs are analyzed to identify user sessions. Then, groups of users which exhibit a common browser behavior (i.e. user profiles) are discovered by applying a fuzzy clustering algorithm to the user sessions. Finally, a knowledge extraction process is carried out to derive associations between user profiles and relevant Web pages to be suggested to users. In particular, a hybrid approach based on the combination of the fuzzy reasoning and the connectionist paradigm is proposed in order to derive know-ledge from session data and represent it in the comprehensible form of fuzzy rules. The derived knowledge is ultimately used to dynamically suggest links to Web pages judged interesting for the current user
Mining Usage Profiles From Access Data Using Fuzzy Clustering
No full textIn this work, we present an approach to clustering Web site users into different groups and generating common user profiles. These profiles are intended to be used to make recommendations by suggesting interesting links to the user. By using a fuzzy clustering algorithm, we enable generation of overlapping clusters that can capture the uncertainty among Web user’s navigation behavior. Preliminary experimental results are presented to show the clusters generated by mining the access log data of a web site
The NEWER system: how to exploit a neuro-fuzzy strategy for Web recommendation
No full textWeb recommendation is a promising technology aimed to predict the needs of users by suggesting them information or services retained interesting according to their preferences. Web recommendation finds in Soft Computing techniques a valid tool to handle with the uncertainty and the ambiguity characterizing the Web and all phases of user interactions with Web sites. The main rationale behind this success seems to be the complementary nature of Soft Computing paradigms that properly combined enable the development of hybrid schemes exploiting the potential of each single paradigm. In this paper, we present NEWER, a neuro-fuzzy Web recommendation system that dynamically suggests interesting pages to the current user. NEWER employs a neuro-fuzzy approach in order to determine categories of users sharing similar interests and to extract a recommendation model in the form of fuzzy rules expressing associations between user categories and relevances of pages. The derived model is used by an online recommendation module to dynamically suggest interesting links. Comparative accuracy results show the effectiveness of NEWER
Computational Intelligence Techniques for Web personalization
No full textComputational Intelligence (CI) paradigms reveal to be potential tools to face under the Web uncertainty. In
particular, CI techniques may be properly exploited to handle Web usage data and develop Web-based applications tailored on
users preferences. The main rationale behind this success is the synergy resulting from CI components, such as fuzzy logic,
neural networks and genetic algorithms. In fact, rather than being competitive, each of these computing paradigms provides
complementary reasoning and searching methods that allow the use of domain knowledge and empirical data to solve complex
problems. This paper focuses on the major Computational Intelligent combinations applied in the context of Web
personalization, by providing different examples of intelligent systems which have been designed to provide Web users with
the information they search, without expecting them to ask for it explicitly. In particular, this paper emphasizes the suitability
of hybrid schemes deriving from the profitable combination of different CI methodologies for the development of effective
Web personalization systems
- …
