![](bg8.png)
tic model Q has few missing values, so, it allows in-
ferring similarity between two given users even when
they have any items rated in common.
7 CONCLUSION AND FUTURE
WORK
The approach presented in this paper is a compo-
nent of a global work, which the aim, is to seman-
tically enhanced collaborative Filtering recommenda-
tion and to resolve the scalability problem by reducing
the dimension. For this purpose, we have designed
a new hybridization technique, which predicts users’
preferences for items based on their inferred prefer-
ences for semantic information. We have defined two
classes of attributes: dependent and non dependent at-
tribute, and presented a suited algorithm for each class
for building user semantic attribute model. The aim
of this paper is to present our approach for building
user semantic attribute model for dependent attribute.
We have defined an algorithm based on Rocchio al-
gorithm and have applied Latent Semantic Analysis
(LSA) for dimension reduction. Our approach pro-
vides solutions to the scalability problem, and alle-
viates the data sparsity problem by reducing the di-
mensionality of data. The experimental results show
that USCF algorithm improves the prediction accu-
racy compared to usage only approach (UBCF and
IBCF) and hybrid algorithm (Average). In addition,
we have shown that applying Rocchio formula on non
dependent attribute, decreases significantly the pre-
diction accuracy compared to results obtained with
machine learning algorithms. Furthermore, we have
experimentally shown that all attributes don’t have the
same importance to users. Finally, experiments have
shown that the combination of relevant attributes en-
hances the recommendations.
An interesting area of future work is to use ma-
chine learning techniques to infer relevant attributes.
We will also study the extension of the user seman-
tic model to non structured data in witch items are
described by free text. Lastly, study how our ap-
proach can provide solution to the cold start prob-
lem in which new user has few ratings. Indeed, CF
cannot provide recommendation because similarities
with others users cannot be computed.
REFERENCES
Adomavicius, G. and Tuzhilin, A. (2005). Toward the next
generation of recommender systems: A survey of the
state-of-the-art and possible extensions. IEEE Trans.
Knowl. Data Eng., 17(6):734–749.
Balabanovic, M. and Shoham, Y. (1997). Fab: Content-
based, collaborative recommendation. Commun.
ACM, 40(3):66–72.
Ben Ticha, S., Roussanaly, A., and Boyer, A. (2011).
User semantic model for hybrid recommender sys-
tems. In The 1st Int. Conf. on Social Eco-Informatics
- SOTICS, Barcelona, Espagne. IARIA.
Ben Ticha, S., Roussanaly, A., Boyer, A., and Bsa
¨
ıes, K.
(2012). User semantic preferences for collaborative
recommendations. In 13th Int. Conf. on E-Commerce
and Web Technologies - EC-Web, pages 203–211, Vi-
enna, Austria. Springer.
Burke, R. D. (2007). Hybrid web recommender systems. In
The Adaptive Web, volume 4321 of Lecture Notes in
Computer Science, pages 377–408. Springer.
Dumais, S. T. (2004). Latent semantic analysis. An-
nual Review of Information Science and Technology,
38(1):188–230.
Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl,
J. (2004). Evaluating collaborative filtering recom-
mender systems. ACM Trans. Inf. Syst., 22(1):5–53.
HetRec2011 (2011). In 2nd Int Workshop on Information
Heterogeneity and Fusion in Recommender Systems.
The 5th ACM Conf. RecSys.
Lops, P., de Gemmis, M., and Semeraro, G. (2011).
Content-based recommender systems: State of the
art and trends. In Recommender Systems Handbook,
pages 73–105. Springer US.
Manzato, M. G. (2012). Discovering latent factors from
movies genres for enhanced recommendation. In The
6th ACM conf. on Recommender systems - RecSys,
pages 249–252, Dublin, Ireland.
Mobasher, B., Jin, X., and Zhou, Y. (2003). Semantically
enhanced collaborative filtering on the web. In 1st Eu-
ropean Web Mining Forum, volume 3209, pages 57–
76, Cavtat-Dubrovnik, Croatia.
Pazzani, M. and Billsus, D. (2007). Content-based recom-
mendation systems. In The Adaptive Web, volume
4321 of Lecture Notes in Computer Science, pages
325–341. Springer Berlin Heidelberg.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and
Riedl, J. (1994). Grouplens: an open architecture for
collaborative filtering of netnews. In The 1994 ACM
conf. on Computer supported cooperative work, pages
175–186, Chapel Hill, North Carolina, USA.
Rocchio, J. (1971). Relevance feedback in information re-
trieval. In Salton, G., editor, The Smart Retrieval Sys-
tem - Experiments in Automatic Document Process-
ing, chapter 14, pages 313–323. Prentice-Hall, Inc.
Salton, G. (1989). Automatic Text Processing. Addison-
Wesley.
Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J.
(2001). Item-based collaborative filtering recommen-
dation algorithms. In The 10 Int. WWW Conf., pages
285–295, Hong Kong, China.
Sen, S., Vig, J., and Riedl, J. (2009). Tagommenders: con-
necting users to items through tags. In The 18th Int
Conf. on WWW, pages 671–680, Madrid, Spain.
Su, X. and Khoshgoftaar, T. M. (2009). A survey of collabo-
rative filtering techniques. Adv. Artificial Intellegence.
WEBIST2014-InternationalConferenceonWebInformationSystemsandTechnologies
212