Filter-Based Product Search Engines With Dynamic Component Ranking

The use of faceted browsing is common on shopping and comparison websites. When dealing with problems of this kind, it is usual practise to apply a specified set of features in a certain order. This tactic suffers from two major flaws that undermine its effectiveness. First things first: before you do anything else, you need to make sure that you set aside a significant amount of time to compile an effective list. Second, if you have a certain number of aspects and all of the products that are relevant to your search are tagged with the same aspect, then that particular aspect is basically worthless. This article presents a method for doing online business that makes use of a dynamic facet ordering system. On the basis of measurements for specificity and dispersion of aspect value dispersion, the entirely automated system assigns ratings to the characteristics and facets that lead to a speedy drill-down for each and every prospective target product. In contrast to the methodologies that are currently in use, the framework takes into consideration the subtleties that are specific to e-commerce. These nuances include the need for several clicks, the grouping of facets according to the traits that they share, and the predominance of numerical facets. In a large-scale simulation and user survey, our approach performed much better than the baseline greedy strategy, the facet list prepared by domain experts, and the state-of-the-art entropy-based solution. These comparisons were made using the same data

A Nine Month Report on Progress Towards a Framework for Evaluating Advanced Search Interfaces considering Information Retrieval and Human Computer Interaction

This is a nine month progress report detailing my research into supporting users in their search for information, where the questions, results or even thei

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

'A Modern Up-To-Date Laptop' -- Vagueness in Natural Language Queries for Product Search

With the rise of voice assistants and an increase in mobile search usage, natural language has become an important query language. So far, most of the current systems are not able to process these queries because of the vagueness and ambiguity in natural language. Users have adapted their query formulation to what they think the search engine is capable of, which adds to their cognitive burden. With our research, we contribute to the design of interactive search systems by investigating the genuine information need in a product search scenario. In a crowd-sourcing experiment, we collected 132 information needs in natural language. We examine the vagueness of the formulations and their match to retailer-generated content and user-generated product reviews. Our findings reveal high variance on the level of vagueness and the potential of user reviews as a source for supporting users with rather vague search intents

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

Next Generation Catalogue: A User’s expectation

Paper presented at International CALIBERSince the days of Cutter, tools to access the resources of libraries are changing their structure and interface rapidly and dramatically to fulfill the dynamic user needs. Today almost every library user comes with expectations set and defined by their experience of using the Web. So the catalogues, which are offered by the libraries, need to operate at the same level of sophistication as other popular Web destinations. The “next generation” library catalog is a tool designed to fit into this shifting environment and move librarianship into a more active role when it comes to increasing the sphere of knowledge. The purpose of the paper to examine the present developments and explores the likely future developments in re-designing the OPAC to support resource discovery. The different ongoing developments follow a unique approach, but one thread that is common in all of them is that they involve a desire to go far beyond the capabilities of legacy catalogues and give library users more powerful and appealing tools

Towards exploratory faceted search systems

In this thesis, we cover what we believe would be the main ingredients of an exploratory search system (ESS). In a nutshell, these are textual queries, facets, visual results, social search and query-by-example. The goal of the thesis is to show how all of these elements could readily be integrated into a typical faceted search system that users are already accustomed to. In this respect, we propose that the future of exploratory search might be a traditional faceted search system, but with the added ingredients of information visualizations and query-by-example. To illustrate our ideas we have built two freely available web applications. The first one, Biomed Search, has been positively received by the community and offers some novel characteristics. First, in order to improve on both precision and recall, Biomed Search indexes not only the text caption but also the text that refers to the image. Second, the interface uses a common pattern of zooming in on a particular search result in order to display more information. User feedback on Biomed Search has hinted towards faceted search, visual search results and query-by-example. The second system, Cloud Mining, is an attempt at implementing the vision set forth in this thesis. The system is a framework used to instantiate ESSs. It offers the novel characteristics of facet views as well as multiple-item based searches combined with textual queries. Cloud Mining paves the way to a completely pluggable search framework, in which every component would be driven by a community of users. The system was tested on large publicly available datasets and all its software components are available under an open source license. The main contributions of this thesis come as lessons learned, suggestions or recommendations as to how to extend the current paradigm of faceted search into the one of exploratory search. The search results and facets should be extended with different views. Query by example should be integrated with Bayesian Sets as it reduces the handling of complex content based searches to choosing the right plugin. Finally, the system should be thought as a framework to instantiate ESSs, in which every one of its component is a community driven plugin. These customized tailored tools, when applied to a dataset of interest, could offer a collective intelligence approach to information overload

The State-of-the-Art of Set Visualization

Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net