![](bg6.png)
cially if it is given long and inaccurate queries. As the
length of the query increase, the system is not able to
find equal or at least very similar queries in the graph,
so the suggested queries are too generic in respect to
the original intent of the user or they lacks of correla-
tion.
Taking a look to the queries that led to good sug-
gestions, we noticed they are manly specific product
names, product types and brands. For this kind of
queries the system is able to reformulate the search
texts for specialization, equivalent reformulation and
parallel movement.
The web application devised to evaluate the qual-
ity has also been employed for measuring the re-
sponse times of the query suggestion system, logging
the time taken for generating the page with the sug-
gestions. The average time has been 0,0059 seconds,
which allows to employ the system in an online real
time environment.
4 CONCLUSIONS
The initial objective was the realization of a solution
in order to enhance the search feature in an web ap-
plication for price comparison implementing a query
suggestion system. We realized a system that could
take advantage from all the available data about the
queries submitted to the web sites, while keeping a
generic approach as much as possible, in order to al-
low the proposed solution to be applicable even on
different search engines.
The implemented system is considered satisfac-
tory in respect to the requirements we had set. This
is confirmed by the experiments where, given 6 mil-
lions queries from a web site logs, the users consider
the suggestions good, measuring a quality of 70% and
a coverage of 37%, which are the queries which lead
to at least eight suggestions.
Thus the performance are good, as the system in
the online phase need about 1,3Gb of memory and it
responds with a latency less then one hundredth of
second.
The most promising possible future developments
involve two aspects of the system. Firstly, the im-
provement of the ranking function, adding more pa-
rameters to consider clicks and relations among sug-
gested queries and click-through rates, thus consider-
ing a linear combination of more factors rather than
just adding the normalized weights from the graphs.
Secondly, the definition of different similarity mea-
sures in place of the Jaccard index.
REFERENCES
Baeza-yates, R. A. (2007). Graphs from Search Engine
Queries.
Baeza-yates, R. A., Hurtado, C. A., and Mendoza, M.
(2004). Query Recommendation Using Query Logs
in Search Engines.
Boldi, P., Bonchi, F., Castillo, C., Donato, D., Gionis, A.,
and Vigna, S. (2008). The query-flow graph: model
and applications. In International Conference on In-
formation and Knowledge Management, pages 609–
618.
Boldi, P., Bonchi, F., Castillo, C., and Vigna, S. (2009).
From ”dango” to ”japanese cakes”: Query reformula-
tion models and patterns. In Web Intelligence, pages
183–190.
Broccolo, D., Frieder, O., Nardini, F. M., Perego, R., and
Silvestri, F. (2010). Incremental Algorithms for Effec-
tive and Efficient Query Recommendation.
Cao, H., Jiang, D., Pei, J., He, Q., Liao, Z., Chen, E., and Li,
H. (2008). Context-aware query suggestion by mining
click-through and session data. In Knowledge Discov-
ery and Data Mining, pages 875–883.
Mat-Hassan M., L. M. (2005). Associating search and nav-
igation behavior through log analysis. Journal of the
American Society for Information Science and Tech-
nology, 56(9):913–934.
M.P. Kato, T. Sakai, K. T. (2011). Query session data vs.
clickthrough data as query suggestion resources. In
ECIR 2011 Workshop on Information Retrieval Over
Query Sessions.
Ortiz-Cordova A., J. B. (2012). Classifying web search
queries to identify high revenue generating customers.
Journal of the American Society for Information Sci-
ence and Technology. cited By (since 1996) 0; Article
in Press.
Pierrakos, D., Paliouras, G., Papatheodorou, C., and Spy-
ropoulos, C. D. (2003). Web usage mining as a
tool for personalization: A survey. User Mod-
eling and User-Adapted Interaction, 13:311–372.
10.1023/A:1026238916441.
Shoppydoo (2012). http://www.shoppydoo.it.
Srivastava, J. and Cooley, R. (2000). Web usage mining:
Discovery and applications of usage patterns from
web data. SIGKDD Explorations, 1:12–23.
Tan, P.-N., Steinbach, M., and Kumar, V. (2005). Introduc-
tion to Data Mining. Addison Wesley.
KDIR2012-InternationalConferenceonKnowledgeDiscoveryandInformationRetrieval
156