Search results for "Web mining"

Advances in Practical Applications of Agents, Multi-Agent Systems, and Sustainability: The PAAMS Collection

2015

This volume presents the papers that have been accepted for the 2015 special sessions of the 13th International Conference on Practical Applications of Agents and Multi-Agent Systems, held at University of Salamanca, Spain, at 3rd-5th June, 2015: Agents Behaviours and Artificial Markets (ABAM); Agents and Mobile Devices (AM); Multi-Agent Systems and Ambient Intelligence (MASMAI); Web Mining and Recommender systems (WebMiRes); Learning, Agents and Formal Languages (LAFLang); Agent-based Modeling of Sustainable Behavior and Green Economies (AMSBGE); Emotional Software Agents (SSESA) and Intelligent Educational Systems (SSIES). The volume also includes the paper accepted for the Doctoral Conso…

Studying the feasibility of a recommender in a citizen web portal based on user modeling and clustering algorithms

2006

This paper presents a methodology to estimate the future success of a collaborative recommender in a citizen web portal. This methodology consists of four stages, three of them are developed in this study. First of all, a user model, which takes into account some usual characteristics of web data, is developed to produce artificial data sets. These data sets are used to carry out a clustering algorithm comparison in the second stage of our approach. This comparison provides information about the suitability of each algorithm in different scenarios. The benchmarked clustering algorithms are the ones that are most commonly used in the literature: c-Means, Fuzzy c-Means, a set of hierarchical …

Automatic User Profile Mapping To Marketing Segments In A Big Data Context

2015

International audience; Within the discussion about the analysis methods for Big Data contexts, semantic technologies often get discarded for reasons of efficiency. While machine learning and statistics are known to have shortcomings when handling natural language, their advantages in terms of performance outweigh potential concerns. We argue that even when handling vast amounts of data, the usage of semantic technologies can be profitable and demonstrate this by developing an ontology-based system for automatically mapping user profiles to pre-defined marketing segments.

A Morphosyntactical Complementary Structure for Searching and Browsing

2006

This paper is a proposal for the construction of a pseudo-net built with precisely defined tokens describing the content and structure of the original WWW. This construction is derived by morphosyntactical analysis and should be structured with a post-processing mechanism. It is provided also an in-depth analysis of requirements and hypothesis to be stated to accomplish with this goal. An in-depth analysis of requirements and hypothesis to be stated to accomplish this goal is also provided. Such derived structure could act as an alternate network theme organization with a compacted version of the original web material. This paper does not describe nor study the post-processing approaches. I…

Web Usage Mining by Neural Hybrid Prediction with Markov Chain Components

2021

This paper presents and evaluates a two-level web usage prediction technique, consisting of a neural network in the first level and contextual component predictors in the second level. We used Markov chains of different orders as contextual predictors to anticipate the next web access based on specific web access history. The role of the neural network is to decide, based on previous behaviour, whose predictor’s output to use. The predicted web resources are then prefetched into the cache of the browser. In this way, we considerably increase the hit rate of the web browser, which shortens the load times. We have determined the optimal configuration of the proposed hybrid predictor on a real…

Web mining e Application Programming Interfaces: caratteristiche, strumenti, prospettive e limiti

2014

Exploring User Behavior in Destination Websites: An Application of Web Mining Techniques

2021

The development of the Internet has so strongly affected the way a tourist destination can be promoted that portals now play an important role in defining the marketing strategies of tourist destinations. Hence, investigating website surfing behavior has become crucial to understanding the needs of potential tourists. Using Web Mining techniques, our study explores the information-seeking behavior of those who log on to a website promoting the island of Sicily, a well known tourist destination in the South of Italy. The study explores whether a tourist’s country of origin affects their information needs and surfing behavior. Actually, our results do show differences in behavior between user…

Analysis of clickstream data with mixture hidden markov models

2021

clickstream data sono un’importante fonte di informazioni per l’ecommerce, sebbene non siano semplici da gestire e convertire queste informazioni in un reale vantaggio competitivo non e un compito banale. In questo articolo, consid- ` eriamo l’applicazione dei mixture hidden Markov model a dati relativi al flusso di clickstream estratti dal portale e-commerce di un’azienda di servizi turistici. Sono stati individuati cluster relativi al comportamento di navigazione degli utenti e alla loro posizione geografica che forniscono indicazioni importanti per lo sviluppo di nuove strategie di business. Clickstream data is an important source of information for businesses, however it is not easy to …

Application of neural network to predict purchases in online store

2016

A key ability of competitive online stores is effective prediction of customers’ purchase intentions as it makes it possible to apply personalized service strategy to convert visitors into buyers and increase sales conversion rates. Data mining and artificial intelligence techniques have proven to be successful in classification and prediction tasks in complex real-time systems, like e-commerce sites. In this paper we proposed a back-propagation neural network model aiming at predicting purchases in active user sessions in a Web store. The neural network training and evaluation was performed using a set of user sessions reconstructed from server log data. The proposed neural network was abl…

Using association rules to assess purchase probability in online stores

2016

The paper addresses the problem of e-customer behavior characterization based on Web server log data. We describe user sessions with the number of session features and aim to identify the features indicating a high probability of making a purchase for two customer groups: traditional customers and innovative customers. We discuss our approach aimed at assessing a purchase probability in a user session depending on categories of viewed products and session features. We apply association rule mining to real online bookstore data. The results show differences in factors indicating a high purchase probability in session for both customer types. The discovered association rules allow us to formu…