A Survey of Collaborative Web Search
Through Collaboration among Search Engine Users to More Relevant Results
Pavel Surynek
Faculty of Mathematics and Physics, Charles University in Prague, Malostranské náměstí 25, Prague, Czech Republic
Keywords: Collaborative Web Search, Social Search, Search Engine, Search Results, Collaborative Filtering,
Recommender Systems, System Integration.
Abstract: A survey on collaborative aspects of web search is presented in this paper. Current state in full-text web
search engines with regards on users collaboration is given. The position of the paper is that it is becoming
increasingly important to learn from other users searches in a collaborative way in order to provide more
relevant results and increase benefit from web search sessions. Recommender systems represent a rich
source of concepts that could be employed to enable collaboration in web search. A discussion of techniques
used in recommender systems is followed by a suggestion of integration web search with recommender sys-
tems. An initial experience with web search powering small academic site is reported finally.
1 INTRODUCTION AND
MOTIVATION
Web search is an area of the information technology
industry where artificial intelligence and particularly
knowledge engineering techniques can be applied
with potentially significant impacts. Currently users
face a still increasing amount of data of many kinds
that can be accessed through web (textual data, mul-
timedia data, automatically collected data – CCTV
records, etc...). Organizing and making these infor-
mation accessible represents a continuous challenge
despite the existence of powerful mainstream search
engines like Bing (Microsoft Corp., 2013), Google
(Google Inc., 2013), and Yahoo! (Yahoo! Inc.,
2013), and also national ones like Yandex (Yandex
Corp., 2013), Baidu (Baidu, Inc., 2013), Naver
(NHN Corp., 2013), Seznam (Seznam.cz and
Lukačovič, 2013), and Jyxo (CET21 and Illich,
2013). When looking at these search engines more
closely it can be observed that from the point of
view of software engineering aspect they represent a
pinnacle of technological achievement in software
industry. However, if the point of view of artificial
intelligence is adopted then it cannot be certainly
said if these search engines are intelligent. For ex-
ample little is utilized by search engines from series
of user’s search queries and little support is provided
for cooperation among users. It is quite reasonable
assumption that a series of queries characterize the
effort of what the user want to find better than the
single query. The typical search engine however
does not help in this effort – users are put into isola-
tion typically which precludes any cooperation and
recommendation from other users based on past
queries. To be honest, for instance the Bing search
engine (more correctly the decision engine) uses
certain technology that provide search results based
on user’s search history and geographical location.
This cannot be always said about other search en-
gines. Another relatively weak point of contempo-
rary search engines is that little is done in under-
standing the search query from the perspective of
natural language processing and understanding
(Chakrabarti, 2003).
1.1 Through Collaboration to better
Experience: A Suggestion
A brief survey of techniques used in contemporary
search engines and suggestion of improvements that
can enable a better user’s experience from the search
session are the main parts of the paper. It is suggest-
ed to employ cooperation among users in their
search effort to achieve the goal. The complete us-
er’s interaction with the search engine will be con-
sidered – that is whole user’s search history as well
as the sequence of latest user’s queries will be taken
into account (Pasca and van Durme, 2007).
331
Surynek P..
A Survey of Collaborative Web Search - Through Collaboration among Search Engine Users to More Relevant Results.
DOI: 10.5220/0004621803310336
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2013), pages 331-336
ISBN: 978-989-8565-81-5
Copyright
c
 2013 SCITEPRESS (Science and Technology Publications, Lda.)

1.2 Web Search and Recommender
Systems
Recent advances in the area of recommender sys-
tems (Ricci et al., 2011) indicate that great im-
provement of user’s benefit in finding desirable
items can be achieved through collaboration among
users. Hence it is suggested to integrate techniques
of recommender systems with web search.
Until the recent announcement of Facebook’s
(Facebook Inc., 2013) social search engine (Graph
Search) in January 2013 (Straley, 2013), recom-
mender systems and search engines have been con-
sidered mostly separately. Since social networks
possesses profiles of millions of users it had a great
opportunity to carry out cooperation among users by
recommending results to users (or items, products,
events, etc...) that have been searched by users simi-
lar to them in terms of the profile similarity. It is
expected that the attempt will initiate interest in
integrations of recommender systems and search
engines.
The focus of this work regarding the integration
of web search and recommender systems is different
than that of used in social networks. It is planned to
target research on operators that do not possess any
user profiles which is quite rare anyway. The data
suggest to be exploited is the user’s interaction and
behavior. So there is a technical limitation in this
sense but on the other hand the limitation represents
a research opportunity and also implies wider range
of potential operators of such integrated cooperative
search service. Certain attempts have been already
done in recommending search results using case
based reasoning on user’s search history (Ross and
Wolfram, 2000; Smyth et al., 2011).
2 SURVEY OF THE CURRENT
STATE IN WEB SEARCH
The search engine (Büttcher et al., 2010) can be
regarded from two different aspects – the software
engineering one and the computational intelligence
one. From the perspective of software engineering
the top level design of traditional search engine is
relatively simple; difficulties appear after looking at
individual building blocks.
The top level operational design consists of the
phase of crawling the web in which textual or mul-
timedia documents are retrieved from the web and
stored into the internal storage. Then a phase of
indexing follows immediately after. A so called
index is build in the indexing phase. The index asso-
ciates searched items, that is words in most cases,
with their occurrence in web documents. After the
index is built users can post their textual queries
through a web interface which are then processed on
the server side and searched in the index. Multiple
iterations of crawling and indexing are made typical-
ly to keep the index up to date while the search ser-
vice is provided without any interruption.
2.1 Algorithmic and Software
Engineering Challenges
One of the most important software engineering
challenges appears in building the index. It is very
challenging to design the index structure capable of
holding the associations of searched items for mil-
lions of web documents (consider that Google has
the index for more than 40e+9 pages; in other major
search providers this number is around 20e+9 pages
(de Kunder, 2013)) and to keep the speed of im-
portant operations such as insertion or search in the
index at the acceptable level. This challenge is ad-
dressed mostly by efficient programming and by
distributing the task. The requirements on the disk
capacity to store the index is also a considerable
issue. Various techniques of compression are usually
employed to make the size of the index manageable
(consider that uncompressed index needs many
times larger space than the indexed documents
themselves).
If no kind of load balancing is considered then
the search phase is relatively easy. First, a certain
kind of parsing of the user’s textual query is made.
Then direct searches into the index are performed
and results are presented to the user. If too many
user’s are expected to post queries then the task
needs to be distributed on several machines while
each of them needs to have access to an up to date
index – so certain kind of distribution and/or syn-
chronization of local indexes is imposed by this
phase as well.
Let us note that modern algorithmic techniques
play a significant role in making the search engines
possible. For example modern data structures de-
rived from trie (Baeza-Yates and Gonnet, 1996;
Fredkin, 1960) such as suffix trees (Ukkonen, 1995;
Mansour et al., 2011) or suffix arrays (Dementiev et
al., 2008; Manber and Myers, 1990), are used as
building blocks of index structures. Important bene-
fits for user’s experience during the search engine
session is directly rooted in these data structures –
let us mention approximate string matching (Navar-
KEOD2013-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment
332

ro, 2001; Navarro et al., 2001) supported by suffix
trees that allows users make typos in their queries.
Another important algorithmic issue that should
be in focus of the search engine designer is the
cache awareness/obliviousness of data structures
(Bender et al., 2005) and algorithms (Frigo et al.,
2012). As the storage for index must be inherently
built in hierarchical manner – from external storage
to CPU cache – this issue is a key for the overall
performance of the search engine. Moreover classi-
cal consideration of cache is reduced on buffering
between the main memory and CPU; in the case of
index storage the situation is more complicated as
there is at least one more layer between main
memory and disk. One more layer is represented by
the distribution in the case of distributed index.
2.2 Computational Intelligence in
Web Search
Into the computational intelligence aspect it is ac-
counted ordering of search results and understanding
user’s search query. It is well known that a break-
through in ordering of search results was made by
the PageRank algorithm (Brin and Page, 1998) that
ranks web page by simulating a so called random
surfers who randomly follows links in web pages or
randomly skips to new web pages. A web page
where many random surfers gather are considered to
be important and results on them appear on the top.
Many other supporting proprietary techniques are
used to improve orderings given by PageRank. It is
necessary to take into account position of searched
term within web documents. Another important
issue which is however out of scope of this study is
security and protection against biasing search results
by generating artificial links – the well known
spamdexing represents one of these infestations.
The understanding of search query has been ad-
dressed in multiple ways. One approach is to analyze
the given textual query by techniques of natural
language processing which includes syntactic and
semantic analysis of the query at the level of sen-
tences (Zhou et al., 2007). The search in the index is
performed not only for terms entered by the user but
also for terms that are semantically related to them.
Semantically related terms are often search in ontol-
ogies.
An interesting example of syntactic analysis of
user queries is represented by the Sherlock Holmes
search engine (commercially known as Morfeo)
(Mareš and Špalek, 2009) which performs Czech
language stemming of searched terms. Similar tech-
niques are implemented in major search engines
mostly for English.
The difficulty of making research in search en-
gines is that it is little publicly known about internal
techniques used by major search providers since this
represents a proprietary know-how and one of the
most valued secrets of each company.
3 A BRIEF SURVEY OF
RECOMMENDER SYSTEMS
The second technology in focus of this paper is rep-
resented by recommender systems (Resnick and
Varian, 1997; Ricci et al., 2011). Great advances
have been made in automated recommending of
various items such as music and movies recently.
The most successful technique for making automat-
ed recommendations is collaborative filtering in
which preferences of many users are processed in
order to make good recommendations for other us-
ers. Knowing what items the user is interested in and
preferences of other users, the system is able to
recommend other items the user may be also inter-
ested in. Contrary to item-based recommendation
(Sarwar et al., 2001), the collaborative filtering is
able to bring novelty from the perspective of the
active user since not only similar items to those
already preferred can be recommended (Melville et
al., 2002) (if items are represented as vectors then
similarity between two items can be calculated as
correlation or as their angle, that is, scalar product).
Recommender systems became widely known
thanks to commercially successful recommender
system of Amazon.com (Linden et al., 2003) and
thanks to Netflix prize (Bell and Koren, 2007). One
of the most successful approaches in recommending
is a so called matrix factorization (Koren et al.,
2009.).
If user preferences are represented in a matrix
where items corresponds to columns and rows to
users then the immediate observation is that this
matrix very sparse. The given sparse matrix can be
submitted to dimensionality reduction methods
(Rennie and Srebro, 2005) that project explicit user
preferences into a different space (of smaller dimen-
sion) where user preferences are represented in more
general categories. If for instance the original matrix
represents preferences of particular movies then the
transformed one can represent preferences of movie
genres. Thus algebraic methods serve here very
efficiently for knowledge mining and knowledge
discovery.
ASurveyofCollaborativeWebSearch-ThroughCollaborationamongSearchEngineUserstoMoreRelevantResults
333

4 COLLABORATIVE WEB
SEARCH
In this preliminary research, it is suggested to inte-
grate techniques from web search with techniques
from recommender systems to enable collaborative
web search implicitly. Integrating of both approach-
es also require to develop new techniques in both
areas inspired by each other. The implicit coopera-
tion means that the user is not required to do any-
thing else than entering search queries into the
search box and picking some of provided results. No
acting in sense of the social networking is expected.
4.1 Research Topics in Collaborative
Web Search
The top level research question is how to make co-
operative recommendations for the user of the web
search engine. This means to suggest what the user
may be interested in from knowing her/his and other
users past short-term and long-term interaction with
the search engine. The aspect of novelty in suggest-
ing interesting items should be also investigated
since the user may not be sure in what she/he is
trying to find and novelty in recommendation could
provide her/him new search directions.
It is expected that combination of feature based
and semantic methods will be used to make efficient
recommendations. Feature based methods include
methods that regard the user’s preference of
searched items as feature vectors and matrices re-
spectively if groups of users are considered, and
these collections of features are processed by alge-
braic and numeric methods such as matrix factoriza-
tion. The semantic approach includes understanding
of searched terms by their meaning in natural lan-
guage and ability to imply what terms are semanti-
cally related. Consider that in the case of search
engine there is a weak association between searched
terms and items (web documents) the user actually
want to find. Hence it is needed to develop tech-
niques that discover these associations automatical-
ly. The source of techniques for this task is machine
learning (Witten and Frank, 2011).
4.2 Technical Issues in Evaluation
Except this top level research direction there are
several minor issues that should be addressed. One
of them is evaluation of suggested techniques. Cur-
rently it is unclear how to evaluate the search engine
in a real-life scenario while scientific attributes of
such evaluation are ensured; that is, repeatability and
reproducibility of results, since interaction with live
users is hard to reproduce. Hence suggestion of
relevant test scenarios and acquisition of real life
data will be also the task within this research topic.
On the other hand web with millions of users repre-
sents a huge pool of opportunities to obtain testing
data and to make experiments.
Another problematic point that is planned to be
addressed is a so called cold start of collaborative
recommendation. If no or few data is collected from
users of particular recommender system then it is
hard to cooperate in suggesting recommendations.
One option plan to overcome this problem is using
semantic information instead of cooperative one and
to gradually increase the cooperative component as
the amount of collected data increases.
5 CONCLUSIONS AND FUTURE
WORK
Two areas are in focus of this paper – web search
and recommender systems. A survey of contempo-
rary mainstream search engines is given while cer-
tain deficiencies rooted in little cooperation among
search engine users are identified. The main sugges-
tion is to improve user’s benefit from search engine
session by enabling collaboration among users to
provide more relevant results. Current techniques
used in recommender systems are surveyed and
integration of web search with suitable recommend-
er techniques is suggested.
It is planned to integrate recommender tech-
niques into our experimental search engine yeRCH.
The search engine is implemented in C++, PHP, and
JavaScript and all the standard search engine fea-
tures have been already implemented. A short pre-
liminary demo of the yeRCH search engine power-
ing full-text search in an academic web site is shown
in the appendix. Data regarding user’s behavior are
being collected and will be utilized in the future
research and development.
ACKNOWLEDGEMENTS
This work is supported by the Charles University in
Prague, Czech Republic within the PRVOUK pro-
ject (section P46) and by the Czech Science Founda-
tion under the contract number GAP103/10/1287.
KEOD2013-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment
334

I would like to thank anonymous reviewers for
their critical comments and suggestions that will
help me in future research of the topic.
REFERENCES
Linden, G., Smith, B., York, J., 2003. Amazon.com Rec-
ommendations: Item-to-Item Collaborative Filtering.
IEEE Internet Computing, Volume 7 (1), pp. 76-80,
http://www.amazon.com/, IEEE Press.
Baeza-Yates, R. A.; Gonnet, G. H., 1996. Fast text search-
ing for regular expressions or automaton searching on
tries. Journal of the ACM, Volume 43 (6), pp. 915–
936, ACM.
Baidu, Inc., 2013. Baidu Search. http://www.baidu.com/,
China, (Accessed on March 2013).
Bell, R. M., Koren, Y., 2007. Lessons from the Netflix
Prize Challenge. SIGKDD Explorations, Volume 9,
pp. 75-79, ACM.
Bender, M. A., Demaine, E. D., Farach-Colton, M., 2005.
Cache-Oblivious B-Trees. SIAM Journal of Compu-
ting, Volume 35(2), pp. 341-358, ACM.
Brin, S., Page, L., 1998. The anatomy of a large-scale
hypertextual Web search engine. Computer Networks
and ISDN Systems, Volume 30, pp. 107–117, Else-
vier.
Büttcher, S., Clarke, C. L. A., Cormack, G., V., 2010.
Information Retrieval: Implementing and Evaluating
Search Engines. MIT Press.
CET21, Illich, M., 2013. Jyxo search / Yoopy.
http://sluzby.yoopy.cz/, Czech Republic, (Accessed on
March 2013).
Chakrabarti, S., 2003. Mining the web - discovering
knowledge from hypertext data, pp. I-XVIII, 1-345,
Morgan Kaufmann.
Dementiev, R., Kärkkäinen, J., Mehnert, J., Sanders, P.,
2008. Better external memory suffix array construc-
tion. ACM Journal of Experimental Algorithmics,
Volume 12, ACM.
Facebook Inc., 2013. facebook - Connect with friends and
the world around you on Facebook. http://
www.facebook.com, USA, (Accessed on March
2013).
Fredkin, E., 1960. Trie Memory. Communications of the
ACM, Volume 3 (9), pp. 490–499, ACM.
Frigo, M., Leiserson, C. E., Prokop, H., Ramachandran,
S., 2012. Cache-Oblivious Algorithms. ACM Transac-
tions on Algorithms, Volume 8(1), ACM.
Google Inc., 2013. Google Search. http://
www.google.com/, USA, (Accessed on March 2013).
Koren, Y., Bell, R. M., Volinsky, C., 2009. Matrix Factor-
ization Techniques for Recommender Systems. IEEE
Computer, Volume 42 (8), pp. 30-37, IEEE Press.
de Kunder, M., 2013. The size of the World Wide Web.
http://www.worldwidewebsize.com/, Netherlands,
(Accessed on March 2013).
Manber, U., Myers, G., 1990. Suffix arrays: a new method
for on-line string searches. Proceedings of the first
annual ACM-SIAM symposium on Discrete algo-
rithms, pp. 319-327, ACM.
Mansour, E., Allam, A., Skiadopoulos, S., Kalnis, P.,
2011. ERA: Efficient Serial and Parallel Suffix Tree
Construction for Very Long Strings. Proceedings of
the VLDB Endowment, Volume 5 (1), pp. 49–60,
University of Michigan.
Mareš, M., Špalek, R., 2009. Sherlock Holmes Search
Engine. http://www.ucw.cz/holmes/, Czech Republic,
(Accessed on March 2013).
Melville, P., Mooney, R. J., Nagarajan, R., 2002. Content-
Boosted Collaborative Filtering for Improved Recom-
mendations. Proceedings of the 18th National Confer-
ence on Artificial Intelligence (AAAI), pp. 187-192,
AAAI Press.
Microsoft Corp., 2013. Bing Search. http://
www.bing.com, USA, (Accessed on March 2013).
Navarro, G., 2001. A guided tour to approximate string
matching. ACM Computing Surveys, Volume 33 (1),
pp. 31–88, ACM, 2001.
Navarro, G., Baeza-Yates, R. A., Sutinen, E., Tarhio, J.,
2001. Indexing Methods for Approximate String
Matching. IEEE Data Engineering Bulletin 24 (4): pp.
19–27, IEEE Press.
NHN Corp., 2013. Naver Search. http://www.naver.com/,
South Korea, (Accessed on March 2013).
Pasca, M., van Durme, B., 2007. What you seek is what
you get: Extraction of class attributes from query logs.
Proceedings of the 20th International Joint Conference
on Artificial Intelligence (IJCAI), pp. 2832-2837,
IJCAI, 2007.
Resnick, P., Varian, H., 1997. Recommender systems.
Communications of the ACM, Volume 40 (3), pp. 56–
58, ACM.
Rennie, J. D. M., Srebro, N., 2005. Fast Maximum Margin
Matrix Factorization for Collaborative Prediction.
Machine Learning, Proceedings of the 22nd Interna-
tional Conference (ICML 2005), pp. 713-719, ACM
International Conference Proceeding Series 119,
ACM.
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (Editors),
2011. Recommender Systems Handbook. Springer
Verlag.
Ross, N., Wolfram, D., 2000. End user searching on the
Internet: An analysis of term pair topics submitted to
the Excite search engine. Journal of the American So-
ciety for Information Science, Volume 51 (10), pp.
949–958, JASIST.
Sarwar, B. M., Karypis, G., Konstan, J. A., Riedl, J., 2001.
Item-based collaborative filtering recommendation al-
gorithms. Proceedings of the 10th International World
Wide Web Conference (WWW 2001), pp. 285-295,
ACM.
Seznam.cz, a. s., Lukačovič, I., 2013. Seznam search.
http://www.seznam.cz/, Czech Republic, (Accessed on
March 2013).
Smyth, B., Freyne, J., Coyle, M., Briggs, P., 2011.
Rec-
ommendation as Collaboration in Web Search. AI
Magazine, Volume 32(3), pp. 35-45, AAAI Press.
ASurveyofCollaborativeWebSearch-ThroughCollaborationamongSearchEngineUserstoMoreRelevantResults
335

Smyth, B., Coyle, M., Briggs, P., 2012. HeyStaks: a real-
world deployment of social search. Proceedings of
Sixth ACM Conference on Recommender Systems
(RecSys 2012), http://www.heystaks.com/, pp. 289-
292, ACM, (Accessed on March 2013).
Straley, B., 2013. Facebook’s Graph Search: the Ultimate
Personalized Discovery Engine? http://
searchenginewatch.com/article/2238590/Facebooks-
Graph-Search-the-Ultimate-Personalized-Discovery-
Engine, Search Engine Watch, January 23, 2013, (Ac-
cessed on March 2013).
Ukkonen, E., 1995. On-line construction of suffix trees.
Algorithmica, Volume 14 (3), pp. 249–260, Springer
Verlag.
Witten, I. H., Frank, E., 2011. Data Mining: Practical
machine learning tools and techniques. Morgan
Kaufmann.
Yahoo! Inc., 2013. Yahoo! Search. http://
www.yahoo.com/, USA, (Accessed on March 2013).
Yandex Corp., 2013. Yandex Search. http://
www.yandex.ru/, Russia, (Accessed on March 2013).
Zhou, Q., Wang, C., Xiong, M., Wang, H., Yu, Y., 2007.
Spark: adapting keyword query to semantic search.
The Semantic Web, pp. 694-707, Springer Verlag,
2007.
APPENDIX
yeRCH Search Engine Experiments
A prototype search engine called yeRCH has been
developing to be able to conduct experiments with
cooperation in web search. yeRCH search engine is
now powering full-text search on the academic web
site of the author’s institute. Data collected from the
search engine operation will be further processed in
order to improve users experience (entered search
terms, clicked through results, etc. are collected).
An initial experiment has been done with the
search engine. If results are summarized, the most
important observation from collected users behav-
iour is that users often use the search to find con-
crete term on the web while few related searches are
invoked. Hence it is considered that adding recom-
mendation of related searches would increase user’s
benefit from the search.
Figure 1: Illustration of using yeRCH search engine pow-
ering full text search on the author’s academic web site.
KEOD2013-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment
336