=Paper=
{{Paper
|id=Vol-1910/paper0104
|storemode=property
|title=Interactive Data Visualization for Product Search
|pdfUrl=https://ceur-ws.org/Vol-1910/paper0104.pdf
|volume=Vol-1910
|authors=Mandy Keck
|dblpUrl=https://dblp.org/rec/conf/chitaly/Keck17
}}
==Interactive Data Visualization for Product Search==
https://ceur-ws.org/Vol-1910/paper0104.pdf
Interactive Data Visualization for Product
Search
Mandy Keck
Technische Universität Dresden, Germany
mandy.keck@tu-dresden.de
Abstract. In complex search scenarios such as planning a vacation or
finding a suitable gift for a friend, at the beginning the user usually does
not know exactly what he is looking for. However, this is the question
that most search interfaces present as first step. This research aims to
analyze approaches for supporting the user in expressing a search query
based on vague motives and ideas and in evaluating the search results
in order to find a suitable search result. Various visualization techniques
and prototypes are developed to support different stages of the search
process and lead to a construction kit for visual search interfaces.
Keywords: Information Visualization, Product Search, Explorative Search,
Search Strategies, Faceted Search, Search by Example, Construction Kit
1 Introduction
Complex search tasks such as finding a suitable car, planning a vacation, or
identifying the perfect investment opportunity can last days or weeks and usually
the user does not know exactly what he is looking for at the beginning. Search
engines offer access to large data volumes and various possibilities to interpret the
users query like providing corrections and suggestions. Besides these technical
advantages, the search paradigm itself did not change a lot during the last years.
Most of the conventional web search interfaces use the well-known search box
or search forms to express the search query and require the user to transform
a possibly vague information need into a specific search query [5]. Users with
low experience in the current search domain or with a vague information need
have problems to formulate their vague ideas into a concrete query. Besides
that, typical search interfaces offer one-dimensional lists with simple sorting and
filtering functions [6]. In contrast, the research area of Information Visualization
provides various techniques to visualize multidimensional data sets to enhance
the quick comprehension, comparison, and analysis of large result sets.
Furthermore, the introduced scenarios correspond to more exploratory forms
of search, which require much more diverse strategies, rather than simply sub-
mitting a query and seeing a list of matching results [18]. Widely used tools
support information access, such as searching on the web, in digital libraries or
product databases, but other stages of the information journey are poorly sup-
ported at the present [1]. This leads to the need of analyzing and understanding
the search process, placing the users and their search behaviors in the focus of
the research and develop a richer repertoire of interface solutions to support
different stages of the search process.
The aim of this research is to analyze the search process from the users
perspective focusing on the use case of a product search. The use cases focus on
complex search tasks with a vague information need such as planning a holiday
or finding a suitable investment opportunity. Furthermore, different visualization
techniques will be evaluated with the aim to enhance the quick interpretation
and analysis of the products that are structured as multidimensional data sets,
and the quick evaluation and comparison of the results.
The previous considerations lead to the following objectives of my PhD work:
1. the examination of approaches to support the user to express the search
query
2. the investigation of different search behaviors and search models to support
different search strategies and stages during the search process
3. an in-depth analysis of methods to present the result set to the user that
supports a quick interpretation and comparison
The paper is organized as follows: Section 2 addresses background informa-
tion and state of the art concerning Information Search and Information Visu-
alization. Section 3 outlines the research methodology and section 4 describes
a proposed approach to support a product search with vague information need.
Section 5 presents briefly different prototypes and evaluation results, which are
a basis for a construction kit for visual search interfaces that is introduced in
section 6. Finally, section 7 concludes the paper and outlines future work.
2 Background and State of the Art
This research spans several areas, such as Information Retrieval, Information
Seeking, Human-Computer Interaction and Information Visualization. Whereas
Information Retrieval focuses on the technologies that support the finding and
presentation of information, Information Seeking is primarily concerned with the
seeking of information and focuses on the users and their search activities [1].
Latter will be introduced in section 2.1, whereas section 2.2 focuses visualization
techniques for multidimensional datasets in the research area of Information
Visualization.
2.1 Information Seeking
Search activities can be distinguished in Lookup and Exploratory Search [14].
Lookup describes the most basic kind of search tasks such as fact retrieval, known
item search, and question answering. Exploratory Search is a more complex
Design Information Domain Search Design
Pattern Need Expertise Strategy Paradigm Layout Content Dimension
Keyword Search A Direct List Visual Abstract 1D
Search By Example A Search Gallery Visual Abstract 1D
Recommendation B Table Visual Abstract n
Faceted Navigation A+B Matrix Abstract 2D
Navigational
Browsing in Categories A+B Search Map Abstract 2D
Structured Results Abstract 1D * n
Concrete High A – Analytical
Vague Low B – Browsing Clustered Abstract Hierarchy
Fig. 1. Design Patterns (left) and Layout Patterns (right) in Product Search
process that usually starts with a vague information need and therefore requires
multiple iterations of learning, investigation, and reformulation of the search
query. The use case of a product search corresponds to exploratory search, with
the difference that product search is not an open-ended search but aims to
conclude with finding a suitable product. Exploratory search scenarios often
start with a vague information need and usually blend two search strategies:
analytical and browsing [14]. In contrast to the formal, analytical strategies -
that depend on careful planning and iterative query reformulation - browsing
strategies are more informal and interactive, can foster serendipity and depend
on recognizing relevant information [6].
Furthermore, two search paradigms are well established in the web search
world: Direct Search and Navigational Search [17]. Direct Search allows users to
simply write their queries in a text box and became enormously popular with web
search engines, such as Google and Yahoo! Search. Text boxes and search forms
are well-suited for lookup-scenarios, in which the user has a concrete idea of the
desired product (e.g. looking for a flight to London). In contrast, Navigational
search systems provide guidance through the use of a taxonomy [17].
I analyzed different design patterns in the context of product search and
assigned to the introduced search strategies and paradigms (see Fig. 1, left).
Design Patterns have emerged as recurring solutions to common problems and
can be adapted to the current context [15]. Most reviewed e-commerce website
provide the well-known keyword search paradigm with design patterns such as
Autosuggest, Autocomplete, Autocorrect, Instant Results, Partial Matches, Search
Within, Scoped Search and Advanced Search (cp. [15], [16]). These design pat-
terns address searches for users with a concrete information need and a better
domain expertise and are not in the scope of this research. In contrast, the design
patterns Search By Example, Recommendation, Faceted Navigation and Brows-
ing in Categories provide alternatives to the keyword paradigm and can be used
for users with a vague information need and little domain knowledge and are
more suitable for further investigation in context of this research.
2.2 Information Visualization
Furthermore, I analyzed different e-commerce websites to identify patterns to
present the result sets. Products were either presented as an image, when the
visual appearance is important e.g. in case of travel destinations or clothes, or
they were presented in an abstract way, mostly through textual descriptions or
using simple diagrams like bar charts or line graphs. Thus, not many attributes
of a product can be presented and one-dimensional lists and galleries are still the
mostly used patterns (see Fig. 1, right). In contrast, the research area of Infor-
mation Visualization offers various techniques for visualizing multidimensional
data, which are needed in the context of product search. Keim distinguishes be-
tween geometric techniques (e.g. scatterplots, parallel coordinates), icon-based
techniques (e.g. star plots, chernoff faces), and pixel-oriented techniques, where
each data value is mapped to a colored pixel [12]. Using icon-based techniques,
each data record becomes a small independent visual object and data attributes
are mapped to graphical attributes of each glyph, such as size, shape, color and
orientation [3].
3 Research Methodology
The research methodology is divided into 5 main steps:
1. Analysis of the current state of the art in Information Seeking to under-
stand the search process and its strategies and to identify limitations and
desiderata
2. Analysis of the current state of art in Information Visualization with respect
to the visualization of products with multidimensional attributes
3. Proposal of a model to support the product search with a vague information
need and the aim to find a suitable product
4. Design, prototyping, and evaluation of different search approaches targeting
different data sets, user experiences, and information needs
5. Decomposition of the prototypes and assignment to different contexts in
product search with the aim to develop a construction kit for visual search
interfaces that is providing patterns, which suit to different data structures
and targets and is supporting the designer in giving inspiration for the de-
velopment of new interfaces
4 Motive-based Search
To get a better understanding of how people seek information and to describe the
process of information search from the users perspective, Kuhlthau performed a
series of studies and identified distinct phases and emotions unique to each phase
[13]. Particularly in the initial stages, uncertainty and anxiety are an integral
part of the process, followed by feelings of confusion, doubt, and frustration in
the exploration phase. Although the first phases include the most complex tasks
for the user, most search applications invest most of their effort in the later
phases [16] and the user is forced to express his vague ideas and motives as a
specific query, which the system can understand.
The goal of this research is to investigate strategies and methods to support
the first stages of the process of information search. Therefore, it is concen-
trated on complex search tasks, such as planning a vacation, in which the user
is unsure of what he is looking for at the beginning. Motive-based Search refines
Explorative Search by specifying the motive and the aim of the process to find
a suitable product. A motive can be defined as the reason for a search as well
as particular conditions such as how much a product should cost and is usu-
ally influenced by emotions and interests of the user. Because this motive is the
starting point of the search task, this type of task is called Motive-based Search
in this thesis.
Exploration Investigation Evaluation
vague INSPIRE TRACE ANALYZE entirety
FORMULATE COLLECT COMPARE
concrete REFORMULATE EXAMINE VERIFY individual
Fig. 2. Aspects and Tasks of Motive-based Search
Based on a workshop with potential users and the research of Marchioni
and Russel-Rose, who consider search as a holistic process, integrating findabil-
ity with analysis and sensemaking [14] [16], three phases of the motive-based
search could be identified: Exploration, Investigation and Evaluation that
contains different tasks that should be supported during the search process and
are explained by using the scenario of planning a vacation (see Fig. 2) [9].
In the beginning of the search, the user decides to book a vacation without a
concrete idea where he wants to go. Based on his motive for the vacation, some
basic conditions and constraints have to be met. In this example, he is looking
for a cheap short trip in the near future and all travels by air can be quickly
discarded because of his fear of flying. In the phase of Exploration, the user
is unsure at the beginning of his search and needs inspiration and guidance to
start the search process. To support the user in creating new ideas, the interface
has to provide functions that broaden his scope of information (see Inspire in
Fig. 2). During the search, the search idea is getting more concrete and leads to
Formulation and Reformulation tasks.
When finding some interesting topic, e.g. a trip to Paris, Rome or Madrid, the
users task is to read thematically relevant information, and to relate this infor-
mation with previous knowledge in order to extend the personal understanding
of the topic. The interface can support the user by offering functions to con-
struct and organize his knowledge space (see Investigation in Fig. 2). The tasks
that should be supported in this phase are described as follows: Trace (shows
the user, where he has been already and helps to orientate in the information
space), Collect (supports the user with collecting and organizing his findings),
Examine (gives more information about an unknown finding and supports the
learning process).
Finally, the user has to grasp the possibilities of combining bits of information
and different alternatives. The task of the interface in the last phase is to help
the user to narrow the scope and to create a focus (see Evaluation in Fig. 2).
The Evaluation is necessary to judge the value of an item or item collection
with respect to the search goal. It is supported by tasks that are leading from
Analyzing the entire result set and Comparing two ore more items to identify
similarities and differences; to a Verification, that is used to confirm if one item
meets all specific criteria.
5 Visual Search Interfaces for Product Search
Ten prototypes were developed, which focus on different search scenarios and
stages of the motive-based search process. In this section, four of these proto-
types are presented based on different data sets and visualization techniques1 .
Each concept supports a particular search strategy to find the desired product
in a very large database with structured or semi-structured data. Further on,
different search domains such as image search, travel search, and financial prod-
ucts are considered. Since the domains entail different user requirements and
expectations, the resulting interface concepts support emotional or analytical
decisions.
5.1 Relation-based Concept
The first concept is based on a folksonomy as data structure organized in a multi-
dimensional classification scheme. The developed DelViz (Deep exploration and
lookup of Visualizations) concept supports different search tasks such as finding
suitable visualizations for a given context, and analysis of the underlying struc-
tured data set to discover relationships between the search results [8]. To support
these search tasks, the application offers two flexible areas: the representation
of the clasffication scheme on the left-hand side, and the visualization projects
presented as thumbnails on the right-hand side. A splitter in the middle can be
used to expand one of these two areas to change the level of detail on either side
(see Fig. 3, top).
5.2 Facet-based Concept
The second concept focuses on data sets that are structured with a faceted classi-
fication. It is based on two visualization techniques that allow the representation
of multidimensional data across a set of parallel axes: parallel coordinates [7] and
1
http://www.visual-search.org provides the associated videos and prototypes
Fig. 3. Top: DelViz Prototype (top): tags can be selected (red) or removed (black) on
the left-hand side, the right-hand side represents generated subsets, Bottom: Parallel
Sets for Travel Search
parallel sets [2]. Figure 3 (bottom) shows an interface designed for travel search
based on the principle of parallel sets. The interface concept combines principles
of Faceted Browsing with the visualization method of parallel sets to support
additional analytical tasks. We developed the interface with the parallel set and
parallel coordinates technique. A user test compared both variations with each
other and has shown that parallel sets are faster and easier to understand with
regard to faceted searches and analysis tasks, whereas parallel coordinates have
advantages in comparison tasks and similarity-based searches [10].
5.3 Recommendation-based Concept
We designed a recommendation-based concept to support and trigger emotion-
ally driven decisions and uses an ontology of concepts as data set that describe a
holiday, such as warm, beach, party and culture. Fig. 4 (top) shows the interfaces
getInspired for a travel search with a vague information need. Instead of a direct
query on the attribute of the result set, the user communicates his preferences
to the system through a selection of concepts represented by expressive images.
Based on the previous decision of the user, the system decides which concepts are
presented in the next refinement step. A user study with 29 participants and 12
different search tasks was conducted to compare the recommendation-based ap-
proach to a strict navigational concept selection. The study measured time and
clicks to solve a given search task and indicated that the recommendation-based
approach was faster and more efficient than the navigational concept. Further-
more, the results show an obvious trend of increased user experience as well as
inspiration while the search result quality has not dropped [4].
5.4 Example-based Concept
Fig. 4 (bottom) shows a visual approach for a similarity search in a financial
product scenario and is created for users with low domain expertise, who cannot
express their information need with filters, like the first and second concept. The
Fig. 4. Top: getInspired Interface with visual concepts for travel searches, Bottom: Vi-
sual Similarity Search: attributes of the reference product can be selected or deselected,
which influence the glyphs on the right side
user starts the search with a given example of one financial product on the left-
hand side and gets more information about its properties, such as risk, maturity,
and other special features for this single product. Then the user can decide for
each attribute if this criterion is important and can select and deselect them,
which influences the similarity algorithm. On the right-hand side, each circular
glyph represents a single product and depicts its most important features, such
as performance over time and investment term.
An early prototypical implementation suggests that this kind of interface is
well suited for the paradigm of search by example and the browsing of a large
structured or semi-structured dataset. Whilst the overall design of the interface
may remain the same for any kind of structured dataset, the iconic representation
should be adapted to the use case [11].
6 Construction Kit for Visual Search Interfaces
Based on the previous introduced prototypes a construction kit was developed,
which aims to support the search process with the aid of visualization. The
construction kit contains different building blocks, introduced in Figure 5. These
building blocks can be combined to a pattern, that is subdivided into three parts:
What: describes the data input
Why: describes the task that has to be solved
How: describes how the pattern is designed
The elements of the parts ”What” and ”How” are influenced by the visualization
taxonomy used in the multidimensional classification scheme for information
visualizations introduced in [8] and the elements of ”Why” refer to the tasks
identified in section 4.
These patterns can be composed to construction plans, that enables to de-
scribe complex search interfaces with the help of three different connectors:
Successive: the patterns are shown successively
Juxtaposed: the patterns are shown next to each other
Superimposed: the patterns are combined with each other in one view
Furthermore, it is possible to combine individual building blocks with reference
blocks to indicate the influence on another pattern (see Figure 6, reference blocks
”B” and ”C”).
In order to explain this composition, Figure 6 shows a construction plan
that has been reverse engineered from a search interface using parallel sets (see
section 5.2). The underlying data set is a faceted classification. The Search Task
is to formulate search queries and to analyze how many results are left in each
node. Pattern A is visualized by using the building blocks area, a rectangular
grid and bars to compare the sizes of data sets. Highlighting is used to refer to
connected streams, and the bars function as a filter to reduce the result set. The
last interaction method influences pattern B and C, hence, it is combined with
two reference blocks (shown as small rectangles attached to the bottom right
of a pattern. Pattern B deals with the underlying faceted classification as well.
Facets can be nominal (location), ordinal (ranking) or quantitative (price). The
Search Task is to analyze the correlations between these facets and to support
the reformulation of search queries. It uses a parallel plot as Layout Structure
with rectangular Grid but with flows instead of lines to represent a set of results.
Single flows can be highlighted. It is combined with pattern A by a superimposed
connector, hence they overlap in one visualization. The bars serve as axes for
WHAT WHY
DATA STRUCTURE SEARCH TASKS
Network 1-Dimensional 2-Dimensional Exploration
vague INSPIRE
Hierarchy Set Spatial
FORMULATE
Faceted Temporal
Classification concrete REFORMULATE
3-Dimensional Multidimensional
Ontology Invesitgation
Computer Table Item A1 A2 An
entirety
..
.
Folksonomy Model
Item 1
Item 2
..
TRACE
.
Item n
COLLECT
ATTRIBUTE TYPE individual EXAMINE
Categorical Ordered Evaluation
entirety ANALYZE
Nominal Ordinal Quantitative
COMPARE
individual VERIFY
HOW
CONTENT GRID
Text Image Form Rectangular Radial
ABC
Point Icon Glyph Line Area Flow
Atomaric Aggregated Triangular Free-form
LAYOUT STRUCTURE
A B C D
1
2
3
Preview List Tiles Bars Matrix Mosaic Scatterplot Line Graph Cluster
Single 1D 2D
Parallel Plot Table Map Node-Link Nested Indented Partitioned
nD Spatial Structured
Inspect Zoom Exclude Remove Adjust Group Expand
INTERACTION
Navigate Pan Rotate Distort Select Append Highlight Sort Reduce
View Transformation Manipulation
Fig. 5. Building Blocks of the Construction Kit
Pattern A Pattern B Pattern C
Faceted Classification Formulate Faceted Classification Reformulate Set Verify
Quantitative Analyze Nominal, Ordinal, Quantitative Analyze Ordinal
Item 1
Item 2
Item 3
Item 4
Area Rectangular Bars Flow Rectangular Parallel Plot Text Rectangular List
Highlight Reduce Highlight Reduce Inspect Highlight
B C C B
Fig. 6. Construction Plan of the Parallel Set Prototype
the parallel plot layout and also as filter in pattern B, which is indicated by the
reference block attached to pattern A, referencing pattern B. Also in pattern B,
the interaction method reduce combined with the reference block indicates the
influence of the filter on pattern C. The underlying Data Structure of pattern
C is an ordered (ordinal ) result set with the Search Task to verify individual
items. All items are ordered in a list of a title (text) in a rectangular grid. More
details are shown for each item by using the inspect interaction method. The
pattern is combined with the juxtaposed connector to pattern A and B and is
influenced by their filtering. The interaction select in pattern C also influences
pattern B, by selecting single items in the list that are highlighted in the other
visualization.
7 Conlusions and Outlook
This paper presents the current stage of the PhD thesis. The presented construc-
tion kit, presents the last phase of the PhD and serves the purpose to support
the designer in quickly creating new visual search interfaces by giving him or
her a set of elements, which can be easily combined with each other. Resulting
patterns can be used for reuse and adaptation in different contexts. The patterns
are conceptually simple and provide a solid foundation for reuse and redesign.
Furthermore, they can be networked in a very flexible way to create complex
search interfaces. However, the construction kit itself provides only syntactic
support for this combination process by offering the elements and the rules to
combine the building blocks. The semantic level is concerned with the content
and its meaning, which will be addressed by providing example patterns based
on existing solutions. This will be addressed in the last phase of my PhD work.
Acknowledgments. I would like to thank my supervisor, Prof. Rainer Groh
for the support and guidance provided in this research. Parts of this research
has been supported by the European Union and the Free State Saxony through
the European Regional Development Fund (ERDF).
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