![](bg8.png)
mean user correlation observed in each category is
always lower than in the entire dataset. Nevertheless,
the results achieved using behavioral profiles are
satisfactory: NDPM is still very close to 0 and F1-
measure shows a classification accuracy in recognizing
relevant items that is almost 84%. This means that
U(PE)
2
is able to recommend “good” items,
although the individual item prediction gets worse.
When we focus on performance issues, we find the
main advantage of grouping users according to their
behavioral profiles before computing
recommendations: the time requested by UPE to
produce recommendations on the whole dataset of
380 users was 5h 47min, while the time requested by
U(PE)
2
was 57min for computing recommendations
and 1h 27min for classifying users into 11 categories.
The total time for completing the process was 2h
24min.
5 CONCLUSIONS
Recommender systems are a powerful technology
that allows a company to get additional value from
its user database. A real problem is that these
systems are being stressed by the huge volume of
user data in existing corporate databases. A strong
research issue is to develop methods that can
improve the scalability of recommender systems,
still producing high-quality recommendations. In
this paper, we have presented a new approach for
collaborative-based recommender systems. It
integrates knowledge about customers stored in
behavioral profiles into the collaborative filtering
algorithm in order to reduce the computational time
required for generating recommendations. The final
goal of the work has been to identify some measures
for evaluating the quality of recommendations. For
this purpose, we have presented the empirical
evaluation of the U(PE)
2
hybrid recommender
system. Our results have highlighted the actual
improvement of the proposed hybrid approach with
respect to a pure collaborative approach. We can
conclude that the proposed technique holds the
promise of allowing collaborative-based algorithms
to scale to large data sets, still producing high-
quality recommendations.
REFERENCES
Balabanovic, M. and Shoham, Y., 1997. Fab: Content-
Based, Collaborative recommendation.
Communications of the ACM, 40 (3), 66-72.
Buono, P., Costabile, M. F., Guida, S., Piccinno, A.,
2002. Integrating User Data and Collaborative
Filtering in a Web Recommendation Systems. In S.
Reich, M.M. Tzagarakis and P.M.E. De Bra (Eds.),
Hypermedia: Openness, Structural Awareness and
Adaptivity, LNCS, vol. 2266, 315-321. Springer, Berlin.
Burke, R., 2002. Hybrid Recommender Systems: Survey
and Experiments. In User Modeling and User-Adapted
Interaction, 12, 331-370. Kluwer Academic
Publishers, the Netherlands.
Frank, E. and Witten, I., 1998. Generating accurate rule
sets without global optimization. In 15
th
International
Conference on Machine Learning, 144–151. Morgan
Kaufmann.
Konstan, J. A., Riedl, J., Borchers, A. and Herlocker, J. L.,
1998. Recommender Systems: A GroupLens Perspective.
In Recommender Systems. Papers from 1998 Workshop.
Technical Report WS-98-08, 60-64. AAAI Press.
Pazzani, M., 1999. A Framework for Collaborative,
Content-Based and Demographic Filtering. In
Artificial Intelligence Review, 393-408.
Resnick, P. and Varian, H. R., 1997. Recommender
Systems. Communications of the ACM, 40 (3),56-58.
Sarwar, B. M., Karypis, G., Konstan, J. and Riedl, J., 2002.
Recommender Systems for Large-Scale E-Commerce:
Scalable Neighborhood Formation Using Clustering. In
Proceedings of the 5
th
International Conference on
Computer and Information Technology (ICCIT 2002).
Schafer, J. B., Konstan, J. and Riedl, J., 1999.
Recommender Systems in E-Commerce. In EC ’99:
Proceedings of the First ACM Conference on
Electronic Commerce, Denver, CO, 158-166.
Sebastiani, F., 2002. Machine Learning in Automated Text
Categorization. ACM Computing Surveys, 34 (1), 1–47.
Semeraro, G., Abbattista, F., Degemmis, M., Licchelli, O.,
Lops, P., Zambetta, F., 2003. Agents, Personalisation,
and Intelligent Applications. In Corchuelo, R., Ruiz
Cortés, A. and Wrembel, R. (Eds.), Technologies
Supporting Business Solutions, Part IV: Data Analysis
and Knowledge Discovery, Chapter 7, 163-186. Nova
Sciences Books and Journals.
Shardanand, U. and Maes, P., 1995. Social Information
Filtering:Algorithms for Automating’Word of Mouth’.
In Proceedings of CHI ’95, Denver, CO., 210-217.
Terveen, L. and Hill, W., 2001. Human-Computer
Collaboration in Recommender Systems. In J. Carroll
(Ed.), Human Computer Interaction in the New
Millenium, Addison-Wesley, New York.
Tuzhilin, A. and Adomavicius, G., 1999. Integrating user
behaviour and collaborative methods in recommender
systems. In
CHI' 99 Workshop Interacting with
Recommender Systems
, Pittsburgh, PA, USA.
Witten, I. H. and Frank, E., 1999. Data Mining: Practical
Machine Learning Tools and Techniques with Java
Implementations. Morgan Kaufmann Publishers, San
Francisco.
Yao, Y. Y., 1995. Measuring Retrieval Effectiveness
Based on User Preference of Documents. Journal of
the American Society for Information Science, 46, 2,
133–145.
A HYBRID COLLABORATIVE RECOMMENDER SYSTEM BASED ON USER PROFILES
169