=Paper=
{{Paper
|id=Vol-1472/IESD_2015_paper_7
|storemode=property
|title=An Adaptive Faceted Search Interface for Structured Product Offers on the Web
|pdfUrl=https://ceur-ws.org/Vol-1472/IESD_2015_paper_7.pdf
|volume=Vol-1472
|dblpUrl=https://dblp.org/rec/conf/semweb/StolzH15a
}}
==An Adaptive Faceted Search Interface for Structured Product Offers on the Web==
https://ceur-ws.org/Vol-1472/IESD_2015_paper_7.pdf
An Adaptive Faceted Search Interface for
Structured Product Offers on the Web
Alex Stolz and Martin Hepp
Universitaet der Bundeswehr Munich, D-85579 Neubiberg, Germany
{alex.stolz,martin.hepp}@unibw.de
Abstract. In the past few years, a growing amount of e-commerce infor-
mation has been published online either as Linked Open Data or embed-
ded as Microdata or RDFa markup inside HTML pages. Unfortunately,
the usage of such data for product search and comparison is hampered
by the products and services being themselves specific and heterogenous
with regard to their relevant characteristics, and by the search process
that involves learning about the option space. In this paper, we present
an adaptive faceted search interface over product offers in RDF. Our
search interface is directly based on the popularity of schema elements
in the data and does not rely on a rigid conceptual schema with hard-
wired product features, thereby being suitable for arbitrary product do-
mains and product evolution. Further it supports learning during the
search process. As a proof of concept of our work, we provide two use
cases, namely one with product offers from an automobile database, and
a second one with real product data collected from the Web.
1 Introduction
Linked Open Data (LOD) has become a popular paradigm for publishing and
consuming data on the Web. In the last few years, there has been a comple-
menting trend towards publishing structured e-commerce data as RDFa and
Microdata markup embedded in HTML Web pages. Primarily based on the
GoodRelations [1] and schema.org1 vocabularies, this markup can be easily con-
verted into RDF and combined with LOD datasets, which forms a promising
data source for novel Web applications and services.
Unfortunately, the usage of such data for product search and comparison
remains an open challenge. Because products and services are specific and het-
erogeneous with regard to their relevant characteristics, the means for exploring
the giant RDF graph of online product data are limited. On one hand, the search
space is huge because of the multi-dimensionality of products, and on the other
hand, the graph of product information is in many branches sparsely populated
due to the limited adoption of such data publication practices. For users it is
thus very difficult to formulate queries without knowing how well conceptual
elements from the schemas are populated and how much they influence the size
and characteristics of the result set.
1
http://schema.org/
2 A. Stolz and M. Hepp
Each search process involves a learning effect about the option space [2,
p. 9], which should be accounted for by search interfaces. Traditional search
models, however, fail to support exploratory searches within the product space:
Keyword searches flatten multi-dimensional product descriptions to simple, one-
dimensional term matches; query formulation with SPARQL is too complex for
the average user and lacks mediation between the conceptual models of the
data and the mental models of human users; and other approaches suggested
for browsing RDF data (e.g. Tabulator [3]) are very low-level and hence hardly
suitable for serious product search.
In this paper, we propose an instance-driven, adaptive faceted search interface
to support the incremental nature and the learning aspect of product search over
structured e-commerce data on the Web. In a prior publication, we have already
evaluated the usability of this kind of search interface with regard to e-commerce
[4]. The present work describes the single components of the faceted search
interface (i.e. the architecture and implementation of the user interface), and
details about the iterative, incremental search strategy applied to the product
domain. In this respect, we also introduce a novel, instance-based search filtering
approach and highlight the role of user feedback in RDF environments. We
then showcase our approach using (a) a homogeneous dataset derived from the
automotive domain, and (b) some real e-commerce data that we have collected
from the Web of Data.
The rest of this paper is structured as follows: Section 2 describes our faceted
search interface over structured product offers; in Sect. 3, we present two use
cases; and finally, Sect. 4 concludes our work and discusses future directions.
2 Adaptive Faceted Search Interface for Product Offers
In the following, we describe an adaptive faceted search interface for product
offers over RDF data.
2.1 Overview of Faceted Search
Faceted search [5], a special form of exploratory search [6], is a well-established
interaction paradigm both recognized in industry (e.g. eBay2 and Amazon3 ) and
academia for guiding users through option spaces [7]. While the term faceted
search is sometimes equated with faceted browsing or faceted navigation (e.g.
[7] or [2, p. 95]), it is often understood as a combination of faceted navigation and
keyword search functionality [5, p. 24]. Substantial research related to faceted
search has also been done in the field of dynamic taxonomies [8,9].
A faceted search interface is based on facet-value pairs. Facets can be com-
pared to mutually orthogonal categories, whereas facet values (or terms) are
instances of these categories. In the context of product search, facets repre-
sent product dimensions (e.g. the features “color” and “material”), whereas facet
2
http://www.ebay.com/
3
http://www.amazon.com/
An Adaptive Faceted Search Interface for Structured Product Offers 3
1
Keywords ... Search
Product Details Results User Dialog
Feature 1 and Feature 2 4
Feature 1 Product 1 Price
seem to be equivalent.
Feature 2 Description Shall I consolidate them?
Value 1 Product Details Yes No
Image
Value 2
Feature 3
Categories
Keywords ... Search
Commercial Properties Product 2 Price
Category 1
Vendors Description
Category 2
Vendor 1 Product Details
Image Category 3
Vendor 2
Category 4
Price Category 5
< 10 2
10-20 Additional Config.s
3
Filter results by
products with images
... Order items by 3
1 2 3 ... 22
lowest price first
Fig. 1. Mock-up of a faceted search interface for e-commerce
values correspond to instances of these dimensions (e.g. “brown” and “wood”).
Products are usually represented by multiple facets and facet values. The selec-
tion within facets of faceted search interfaces maps to boolean expressions for the
filtering of the option space. While a choice of multiple facets generally leads to
their conjunction (e.g. “color” is “brown” and “material” is “wood”), multiple facet
values are mostly combined using disjunction (e.g. “color” is “brown” or “red”)
(cf. [10]). In set-theoretic terms, conjunction corresponds to the intersection of
items, and disjunction to their union.
Faceted search interfaces dynamically adapt to user interaction. In other
words, the facet views update on reducing or expanding the option space. Fur-
thermore, the faceted navigation paradigm does normally not lead to dead ends
[11], i.e. an empty result set, because a user is only shown facets for which in-
stance data exists. Unlike parametric searches, where a user is forced into a
sequential search order (e.g. first choose the camera type, then the focal length,
after that the picture resolution, and finally the color), faceted search allows
users to drill down the search space in any preferred order that best suits their
individual information needs. Adaptive faceted search interfaces further adjust
themselves (i.e. their structure) to the underlying data, sometimes leading to a
repeated, dynamic reorganization of the filtering options.
2.2 Faceted Search User Interface
In Fig. 1, we show a sketch of a general faceted search interface over e-commerce
data. The faceted search interface combines the two interaction paradigms key-
word search and faceted navigation. As illustrated in the graphic, the keyword
4 A. Stolz and M. Hepp
SPARQL endpoint selection
Tooltips
Expandable
facets
Product details link
Normalized
price values
Price ranges
Pagination
Fig. 2. Screenshot of our faceted product search interface
search field (1) is placed prominently at the top of the search interface, and the
boxes surrounding the result list (2) at the center represent the faceted naviga-
tion controls (3). The “User Dialog” box (4), displayed on the upper right corner,
can further provide a means to unobtrusively incorporate user feedback.
The user interface layout in Fig. 1 served as the baseline for developing the
faceted search interface depicted in Fig. 2. It effectively integrates product de-
tails, product category information, and commercial properties related to prod-
uct offers. The results displayed in the provided screenshot are based on toy data
modeled using the Vehicle Sales Ontology (VSO)4 . The most interesting facts
such as product image, name, description, and price are summarized in the result
list. Nonetheless, for every item in the result list, a link is provided that, when
clicked, opens a window where the full product details are shown. If the size of
the result set exceeds ten items, then the remaining results are outsourced to
other result pages that can be accessed via pagination controls [2, pp. 110-116].
This shall prevent the user from possible information overload. The part left to
the result list is dedicated to product-related details and commercial properties.
It mainly includes product features and prices, but also manufacturers, vendors,
payment options, or business functions. The right part features a category fil-
4
http://purl.org/vso/
An Adaptive Faceted Search Interface for Structured Product Offers 5
ter, along with additional filter configurations such as to exclude product offers
without images or to revert the result order. The search interface further makes
heavy use of tooltips that help users at better understanding the option space.
Our search interface is instance-driven and schema-agnostic with respect to
the generation of the facet views. Instead of relying on a fixed schema, the data
is the first-class citizen that specifies the appearance of the user interface. In
other words, the search interface is directly based on the constraints imposed on
the underlying RDF data and dynamically adapts with the availability of the
data, such as the presence of product features or categories. Accordingly, our
approach is able to flexibly cope with structured product data from diverse data
sources, as long as it complies with the e-commerce meta-model implied by the
GoodRelations vocabulary [1]. This allows for useful guided navigation paths
even in the absence of richly axiomatized products.
Keyword Search. We incorporated two forms of keyword searches into our
faceted search prototype: A first one for product offers, and a second one for
searching within product categories. The keyword searches match terms within
textual properties attached to objects, which includes the names and descriptions
found in product offers, instances, and models, and the labels and comments for
product categories. An autocomplete feature helps the user with term sugges-
tions. It is based on a light-weight SPARQL query executed over the names and
labels of products and product categories, respectively.
Simple keyword search functionality can be obtained using the SPARQL
CONTAINS function [12], even though such queries are typically very costly
for large datasets, as most implementations iterate over all relevant objects.
Some SPARQL endpoints thus permit operations over optimized full-text indexes
built from textual properties in the available data. Such complementary full-text
search engines like Lucene [13] support, in addition to exact term matches, wild-
card queries or fuzzy string matches based on given threshold limits [13, pp.
99-101]. Our prototype relies on Lucene, as long as it is supported by the under-
lying SPARQL endpoint. Otherwise, we fall back to the much slower SPARQL
CONTAINS function.
Faceted Navigation. The exploratory search capability is provided through
the faceted navigation controls that complement the keyword searches. Faceted
navigation uses boolean constraint filtering based on product dimensions, com-
mercial properties of product offers, and product type information.
As the product features facet view in Fig. 2 suggests, product features are
initially displayed in a compact form and expanded to their corresponding values
as the user clicks on them. This mechanism is illustrated in Fig. 3. A selection
of multiple product features leads to their conjunction (logical and ), i.e. items
to appear in the result list need to match every selected feature. By contrast, a
disjunctive approach is used (logical or ) among several facet values. For example,
imagine that a user adds a second payment option to a payment method that
6 A. Stolz and M. Hepp
Fig. 3. Iteratively expanded navigation controls
was already selected before. Matching candidate offers then have to accept at
least one of the two selected payment options.
The possible facet values correspond to qualitative, quantitative, and
datatype properties in OWL. Unlike qualitative or datatype values that may be
implemented with checkboxes that a user can click on, quantitative values can
cover an indefinite range for which checkboxes would be suboptimal. A popular
technique is to group quantitative values into discrete classes given as range
intervals (e.g. “$ 0-20”, “$ 20-50”, etc.). Our approach even uses a range slider as
demonstrated on the price view in Fig. 2 and the total of gears facet in Fig. 3,
respectively. To build up the range slider, we need to generate a useful number
of classes with each having the same width. In order to obtain a proper class
width, the interval between the minimum and maximum value is divided by
the number of classes, which is essentially the instance count with a specified
upper limit (e.g. a maximum of 30 classes). The height of the single bars is
calculated relative to the class with the highest frequency of instances. The scale
of the bars’ height is logarithmic with a fixed maximum height. Thanks to the
frequency of values indicated for every class, a user is able to quickly gauge the
possible outcomes of his filtering decisions on the result set. While this approach
works well for point values, we need to rely on a heuristic for range intervals:
For closed intervals, we use the lower boundary for the classifying of numeric
values into classes, whereas for open intervals, we take the actual value that is
available.
2.3 Implementation
From a conceptual point of view, the front-end of the application is backed by
data from an RDF store accessible through a SPARQL endpoint, which can be
configured as per the endpoint selection dropdown menu in Fig. 2.
An Adaptive Faceted Search Interface for Structured Product Offers 7
The front-end of the application was realized using HTML 5 for static con-
tent, JavaScript and JQuery for user interaction, and Twitter Bootstrap for page
responsiveness. Wherever possible, human-readable labels are shown in the user
interface instead of long and cryptic URIs for classes, instances, and properties.
Provided that they have been loaded into the RDF store before, labels can be
extracted from product vocabularies and instance data. The back-end is based
on the Python programming language and the Jinja 2 templating engine for
generating custom-tailored SPARQL queries, that are then submitted to the
SPARQL endpoint. The application can be run on any general-purpose Apache
Web server instance where the mod_python and mod_wsgi modules are active.
If supported by the SPARQL endpoint, keyword searches execute over a full-text
search engine such as Lucene [13].
Every facet view of the user interface is generated by executing its own,
unique SPARQL SELECT query, which has to take into account the current set
of constraints. It was not feasible to formulate one single, comprehensive query
that would encompass the data to create all facet views. First, due to the fact that
each facet view uses different aggregate functions over the data (e.g. group results
by manufacturers vs. vendors). And second, because large and complex queries
are very costly. Facet-value pairs of faceted search interfaces are represented in
RDF by properties and instances (or values). Similarly, conjunction of facets
and disjunction of facet values within a facet are realized in SPARQL using a
sequence of triple patterns and UNION statements, respectively. In Listing 1.1,
we outline a simplified SPARQL SELECT query to generate the facet view for
product prices. Let us suppose that a user has chosen to select products that are
manufactured either by Apple Inc. (e.g. URI http://www.apple.com/#company)
or by Microsoft Corporation (e.g. URI http://www.microsoft.com/#company),
i.e. he has checked these both organizations in the facet view for manufacturers.
The resulting filter constraint is a UNION clause in SPARQL. Please note also
that the number of affected instances is counted in the projection part of the
query, which is further displayed to the user through the search interface.
As of writing this paper, we had successfully tested our prototype with three
different SPARQL endpoints compliant with the W3C SPARQL 1.1 standard,
namely Virtuoso Open Source Edition5 , the Jena-TDB-based Fuseki SPARQL
server6 , and Stardog7 .
2.4 Incremental Search Strategy
We understand product search as an iterative process (cf. [4]). On every search
step, the user is presented with dynamic facet and result views that represent
the search space pruned according to the current selection. Product search sup-
posedly ends as the user is satisfied with the results or not able to find a path
leading him to better search results. Until then, the search process may move
across multiple search paradigms. Based on the quality of the results, several
5
http://virtuoso.openlinksw.com/dataspace/doc/dav/wiki/Main/
6
http://jena.apache.org/documentation/serving_data/
7
http://stardog.com/
8 A. Stolz and M. Hepp
PREFIX gr:
SELECT (COUNT(DISTINCT ?offer) AS ?cnt) ?pspec ?price ?code
WHERE {
# price-specific graph pattern
?offer a gr:Offering ;
gr:includes ?product ;
gr:hasPriceSpecification ?pspec .
?pspec a gr:UnitPriceSpecification ;
gr:hasCurrencyValue ?price ;
gr:hasCurrency ?code .
# the next part of the graph pattern is common to all facet views
## filter constraint on manufacturer
{ ?product gr:hasManufacturer } UNION
{ ?product gr:hasManufacturer }
## additional filter constraints ...
}
GROUP BY ?pspec ?price ?code
ORDER BY ?price
Listing 1.1. Simplified SPARQL SELECT query to generate the facet view for prices
with a constraint on the manufacturer
iterations with possibly different search paradigms might be necessary. As illus-
trated in Fig. 4, the user might start with keyword search, faceted navigation,
or directly head to the product details page of an item from the initial result
set. The search interface may also accept user feedback that is incorporated into
the graph with product data, as we will explain in Sect. 2.6.
The task of presenting optimal facets to users is a key challenge towards
increasing the search efficiency in an incremental search. A user would ideally
be presented, in every search step, the options (facets and facet values) that
yield the best possible partition of the search space (and create the highest
utility to the user). At the time of writing this paper, the splitting strategy
employed for our prototype was relying on presenting to the user the five most
frequent facets and facet values in the data (cf. [14]). Nevertheless, the authors
are aware of several weaknesses that this methodology is suffering from. E.g., if
all or only very few items exhibit a certain feature or feature-value pair, then the
current algorithm tends to over- or underrate their value for the search progress.
Similarly, the algorithm is misguided as multiple instances of the same feature
belong to a single item. Superior approaches have been suggested in literature,
e.g. [14] mention some popular facet-pair suggestions strategies, namely relying
on frequency, probability, and the information gain. The authors in [15] further
give an overview over different metrics appropriate for product search to help
decide which facets shall be presented to the user. Investigating these alternative
presentation strategies is planned as future work for our prototype.
An Adaptive Faceted Search Interface for Structured Product Offers 9
Keyword
Search
Search Faceted User Search
Start Navigation Feedback Stop
Instance-based
Search Filtering
Fig. 4. Iterative product search across multiple search paradigms
2.5 Instance-based Search Filtering
As a complement to faceted search filters over aggregated data, we herein present
a novel idea for supporting the incremental search process, namely instance-based
search filtering (see Fig. 5). Occasionally, a user might be looking at the details
of a product offer and discover features that he intends to consider for the next
filtering steps in the search process. Now, by going back to the result list, the
user would lose track of the respective features and only find them by chance
in the list of displayed product features, namely if they are ranked high with
respect to the current items in the option space. Of course, the same holds true
for feature values and individuals. From a user interaction point of view, we
can prevent this level of indirection by letting the user apply filters directly
from the product details page. As a nice side effect, this solution facilitates the
comparison among similar products via navigating across a collection of items
that have some product features in common. Even though not entirely the same,
this approach is in a way similar to the pivoting operation in user interfaces (e.g.
[16, pp. 83-84], [17]), where a concept can be found through shared features with
another concept. In order for this to work well, the properties in the RDF graph
need to be consolidated first, which is a research problem on its own that we are
not going to further discuss in this paper.
2.6 User Feedback with Conceptual Consolidation
For product search with incremental learning, it is not only vital to assist in
navigating and pruning the option space, but also to actively engage the user in
the search process, to incorporate user feedback, and to exploit user intelligence.
As already depicted in the mock-up in Fig. 1, a viable approach is to integrate a
user dialog in the search interface. This approach goes one step beyond the tra-
ditional relevance feedback method (e.g. [18, pp. 178-179], [10]), where systems
10 A. Stolz and M. Hepp
Fig. 5. Product details window with instance-based search filtering
explicitly, or implicitly via user behavior, collect information about the relevance
of the results presented to the user.
It is outside the scope of this paper to mention here all possibilities of pro-
viding explicit user feedback in search systems. Yet, a user interface could ask
the user for help in consolidating conceptual correspondences, e.g. to approve a
possible match of the two features gr:hasGTIN-14 and ex:productId (possibly
by way of presenting an instance for each of the two properties), leading to the
following equivalence axiom:
gr:hasGTIN-14 owl:equivalentProperty ex:productId .
In the current state of our implementation, we accept user feedback in the form
of a dialog window that pops up as there exist possibly interesting super-concepts
with respect to the concept in regard (see Fig. 6). On this account, a user is able
to expand the search scope, e.g. going from an automobile (vso:Automobile) to
its super-concept motorized road vehicle (vso:MotorizedRoadVehicle).
:automobile_instance1 a gr:MotorizedRoadVehicle .
:automobile_instance2 a gr:MotorizedRoadVehicle .
Technically, this method offers a guided way to materialize inference rules.
An Adaptive Faceted Search Interface for Structured Product Offers 11
Fig. 6. User feedback dialog window
In an RDF environment, corresponding axioms can be easily added to the
existing data as named RDF graphs – potentially managed on a per-user ba-
sis. The named graphs can then persist in the RDF store to reflect past user
experiences, or otherwise be cleared before a user starts a new search.
3 Use Cases
During the development of our adaptive faceted search interface, we set up two
slightly different use cases. The first one uses product offers and properties de-
rived from a car listing Web site, and the second one is based on real e-commerce
data from the Web. Both use cases have been published on the Web in a combined
demo application8 . We further made the respective (raw and random sampled)
datasets available online9 . A thorough user study of our application has already
been conducted in a prior publication yielding promising results [4].
3.1 Use Case with Product Data from a Car Listing Web Site
Our first use case serves to illustrate how the search interface works with densely
populated product data and a homogeneous dataset, as it is mostly the case
for vertical search engines. More precisely, we have crawled a random sample of
automobile offers from the car listing Web site mobile.de (i.e. as much as we could
collect from the visible part of the Web pages). Our random sample originally
consisted of 875 automobile offers, but due to overly complex SPARQL queries
for the restricted hardware at hand (dual core processor clocked at 2.6 GHz and
with 16 MB cache size, 4 GB RAM), we had to reduce it to 25 instances. In
8
http://www.ebusiness-unibw.org/tools/product-search/
9
http://www.ebusiness-unibw.org/tools/product-search/datasets/
12 A. Stolz and M. Hepp
particular, we selected 50 result pages at random (based on a URL template
with a random page number, a lower bound, and an upper bound for the price)
with 20 results each, summing up to 1000 automobile offers. Effectively, we
could obtain 875 car offers. Of these 875 car offers we again picked a random
sample of 25 car offers that we loaded into our demonstrator. Quite clearly, there
exist more appropriate technologies (e.g. relational databases) than the Semantic
Web to implement this specific use case in terms of efficiency and ease of use,
but they complicate data integration (e.g. include information from DBPedia)
and oppose schema flexibility during run-time. The most obvious finding in this
use case is that due to the high data quality of such a Web site (i.e. almost
every car offer is populated exhaustively), drilling down the option space based
on feature values works quite well. However, as almost every automobile offer
uses the same features, the discriminatory value of product features is low. The
present use case can be accessed from the online demo by selecting the SPARQL
endpoint with the “automobile dataset”.
3.2 Use Case with Real Product Data From the Web
We set up another use case that operates over real e-commerce data from the
Web. We collected product offer data by crawling Web shops with household
appliances. More precisely, we selected shops from the Rakuten Deutschland
platform10 that were classified into categories related to the general topic house-
hold. By that we could obtain 23 Web shops, from which 17 shops contained
GoodRelations [1] markup. Thanks to earlier research [19], we have already been
in the possession of high-quality, structured product model data by BSH11 in
the form of a BMEcat catalog. We used that one to augment the product offer
data from the Web crawl with product features to enable deep product compar-
ison. Furthermore, we executed cleansing and consolidation rules on the data
such as to convert all price specifications to a common currency “Euros”. For
the purposes of this paper, we abstract from possible challenges related to this
task. The interesting aspect of this use case is the integration of various, hetero-
geneous data sources in a single faceted search interface for products. Because
the product features are mainly coming from a single BMEcat product catalog,
the product features are homogeneous as well. However, because the product
types are rather diversified (e.g. coffee machine vs. vacuum cleaner), the variety
in product features is higher than in the previous use case. A live demonstration
of this use case with real product data is available via the provided online tool
by selecting the SPARQL endpoint that contains the “household crawl data”.
4 Conclusions and Future Work
In this paper, we have proposed a data-driven, adaptive faceted search interface
as a means to navigate the giant but often sparse graph of e-commerce data
10
http://www.rakuten.de/, a hosting platform for online shops.
11
Bosch und Siemens Haushaltsgeräte GmbH, a manufacturer specializing on house-
hold appliances.
An Adaptive Faceted Search Interface for Structured Product Offers 13
on the Web. Our prototype enables incremental, multi-parametric searches over
RDF data based on distinguishing properties and attributes of product items,
provides explicit support for user learning about the option space, and allows to
enhance the conceptual consolidation during the search process. As the search
interface does not rely on a rigid conceptual schema with hard-wired product
features, it is suitable for arbitrary product domains and product evolution. We
have demonstrated the viability of our approach by setting up two use cases with
(a) data derived from a car listing Web site, and (b) real product data collected
from the Web.
As future work, we envision to enhance our work by more accurate and
context-sensitive user dialogs, personalization and diversification of facet and
result views, and in particular to utilize metrics for improving the efficiency of
the search process, i.e. to propose features and values that best possible partition
the search space and promise the highest utility to a given information need. We
will also try to incorporate machine-learning components.
Acknowledgments. The authors would like to thank Florian Ott for the kind
support in setting up and hosting the infrastructure of our search prototype.
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