Collaborative personalised dynamic faceted search

Information retrieval systems are facing challenges due to the overwhelming volume of available information online. It leads to the need of search features that have the capability to provide relevant information for searchers. Dynamic faceted search has been one of the potential tools to provide a list of multiple facets for searchers to filter their contents. However, being a dynamic system, some irrelevant or unimportant facets could be produced. To develop an effective dynamic faceted search, personalised facet selection is an important mechanism to create an appropriate personalised facet list. Most current systems have derived the searchers' interests from their own profiles. However, interests from the past may not be adequate to predict current interest due to human information-seeking behaviour. Incorporating current interests from other people's opinions to predict the interests of individual person is an alternative way to develop personalisation which is called Collaborative approach. This research aims to investigate the incorporation of a Collaborative approach to personalise facet selection. This study introduces the Artificial Neural Network (ANN)-based collaborative personalisation architecture framework and Relation-aware Collaborative AutoEncoder model (RCAE) with embedding methodology for modelling and predicting the interests in multiple facets. The study showed that incorporating collaborative approach into the proposed framework for facet selection is capable to enhance the performance of personalisation model in facet selection in comparison to the state-of-the-art techniques

Intelligent Information Systems for Web Product Search

Over the last few years, we have experienced an increase in online shopping. Consequently, there is a need for efficient and effective product search engines. The rapid growth of e-commerce, however, has also introduced some challenges. Studies show that users can get overwhelmed by the information and offerings presented online while searching for products. In an attempt to lighten this information overload burden on consumers, there are several product search engines that aggregate product descriptions and price information from the Web and allow the user to easily query this information. Most of these search engines expect to receive the data from the participating Web shops in a specific format, which means Web shops need to transform their data more than once, as each product search engine requires a different format. Because currently most product information aggregation services rely on Web shops to send them their data, there is a big opportunity for solutions that aim to tackle this problem using a more automated approach. This dissertation addresses key aspects of implementing such a system, including hierarchical product classification, entity resolution, ontology population and schema mapping, and lastly, the optimization of faceted user interfaces. The findings of this work show us how one can design Web product search engines that automatically aggregate product information while allowing users to perform effective and efficient queries