Maria Alessandra Torsello

Similarity-Based Fuzzy Clustering for User Profiling

User profiling is a fundamental task in Web personalization. Fuzzy clustering is a valid approach to derive user profiles by capturing similar user interests from Web usage data available in log files. Often, fuzzy clustering is based on the assumption that data lay on an Euclidean space; however, clustering based on Euclidean distance can lead the clustering process to find user representations that do not capture the semantic information incorporated in the original Web usage data. In this paper, we propose a different approach to express similarity between Web users. The measure is based on the evaluation of similarity between fuzzy sets. The proposed measure is employed in a relational fuzzy clustering algorithm to discover clusters embedded in the Web usage data and derive profiles modeling the real user preferences. An application example on usage data extracted from log files of a sample Web site is reported and a comparison with the results obtained using the cosine measure is shown to demonstrate the effectiveness of the proposed similarity measure.

NEWER: A system for NEuro-fuzzy WEb Recommendation

In 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.

Computational Intelligence techniques for Web personalization

Computational 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.

Shape annotation by semi-supervised fuzzy clustering

Abstract Image annotation is an important and challenging task when managing large image collections. In this paper, a fuzzy shape annotation approach for semi-automatic image annotation is presented. A fuzzy clustering process guided by partial supervision is applied to shapes represented by Fourier descriptors in order to derive a set of shape prototypes representative of a number of semantic categories. Next, prototypes are manually annotated by attaching textual labels related to semantic categories. Based on the labeled proto-types, a new shape is automatically labeled by associating a fuzzy set that provides membership degrees of the shape to all semantic categories. The proposed annotation approach provides an innovative indexing method for shape-based image retrieval. Indeed, shape prototypes represent an inter-mediate indexing level that allows a faster retrieval process since a query is matched against prototypes, instead of the whole shape database, resulting in a speed up of the retrieval. The proposed approach is tested on synthetic and real-word images in order to show its suitability.

Web User Profiling Using Fuzzy Clustering

Web personalization is the process of customizing a Web site to the preferences of users, according to the knowledge gained from usage data in the form of user profiles. In this work, we experimentally evaluate a fuzzy clustering approach for the discovery of usage profiles that can be effective in Web personalization. The approach derives profiles in the form of clusters extracted from preprocessed Web usage data. The use of a fuzzy clustering algorithm enable the generation of overlapping clusters that can capture the uncertainty among Web users navigation behavior based on their interest. Preliminary experimental results are presented to show the clusters generated by mining the access log data of a Web site.

Modeling User Preferences through Adaptive Fuzzy Profiles

Adaptive software systems are systems that tailor their behavior to each user on the basis of a personalization process. The efficacy of this process is strictly connected with the possibility of an automatic detection of preference profiles, through the analysis of the users’ behavior during their interactions with the system. The definition of such profiles should take into account imprecision and gradedness, two features that justify the use of fuzzy sets for their representation. This paper proposes a model for representing preference profiles through fuzzy sets. The model’s strategy for adapting profiles to user preferences is to record the sequence of accessed resources by each user, and to update preference profiles accordingly so as to suggest similar resources at next user accesses. Profile adaption is performed continuously, but in earlier stages it is more sensitive to updates (plastic phase) while in later stages it is less sensitive (stable phase) to allow resource suggestion. Simulation results are reported to show the effectiveness of the proposed approach.

Fuzzy image labeling by partially supervised shape clustering

In this paper, a fuzzy shape annotation approach for automatic image labeling is presented. A fuzzy clustering process guided by partial supervision is applied to shapes represented by Fourier descriptors in order to derive a set of shape prototypes representative of a number of semantic categories. Next, prototypes are manually annotated by attaching textual labels related to semantic categories. Based on the labeled prototypes, a new shape is automatically labeled by associating a fuzzy set that provides membership degrees of the shape to all semantic categories. Experimental results are provided in order to show the suitability of the proposed approach.

Innovations in Web Personalization

The diffusion of the Web and the huge amount of information available online have given rise to the urgent need for systems able to intelligently assist users, when they browse the network. Web personalization offers this invaluable opportunity, representing one of the most important technologies required by an ever increasing number of real-world applications. This chapter presents an overview of the Web personalization in the endeavor of Intelligent systems.

Categorization of Web Users by Fuzzy Clustering

Categorization of users is a fundamental task in Web 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.

A fuzzy set approach for shape-based image annotation

In this paper, we present a shape labeling approach for automatic image annotation. A fuzzy clustering process is applied to shapes represented by Fourier descriptors in order to derive a set of shape prototypes. Then, prototypes are manually annotated by textual labels corresponding to semantic categories. Based on the labeled prototypes, a new shape is automatically labeled by associating a fuzzy set that provides membership degrees of the shape to all semantic classes. Preliminary results show the suitability of the proposed approach to image annotation by encouraging its application in wider application contexts.