Subjective assessments like ‘beautiful’ and
‘breathtaking’ are assigned to items by users and are
commonly found in reviews on many online sites. Analyzing
the links between these SAs and items can help improve the
recommendation accuracy. We propose a different method
which links a taxonomy of items to a taxonomy of SAs to
capture user’s interests in detail.
Vaishnavi Pakhode : MIT College of Engineering,
Pune, Maharashtra 411038, India
Advait Pakhode : MIT College of Engineering,
Pune, Maharashtra 411038, India
Nagesh Jadhav : MIT College of Engineering,
Pune, Maharashtra 411038, India
Recommendation System
Collaborative Filtering
Subjective Assessments
In this study, we have implemented a novel method for the
measurement of similarity of users. Our method groups the
SAs assigned by the users to items in SC and the SAs/SCs
reflect the classes in which they are included. Our method
computes the similarities of users based on the SAs/SCs
assigned to items and those assigned to item classes.
[1] Makoto Nakatsuji and Yasuhiro Fujiwara,” Linked
taxonomies to capture users’ subjective assessments of
items to facilitate accurate collaborative filtering”,
ScienceDirect, Artificial Intelligence 207 (2014) 52–68.
[2] I.Cantador, I.Konstas, J.M.Jose, Categorizing social tags
to improve folksonomy-based recommendations,
J.WebSemant.9 (2011)1–15.
[3] Y.Zhen, W.-J.Li, D.-Y.Yeung, TagiCoFi,”tag informed
collaborative filtering”, Proc. Rec Sys’ 09 , pp.69–76.
[4] J.L. Herlocker, J.A. Konstan, L.G. Terveen, J.T. Riedl
“Evaluating collaborative filtering recommender
systems”, in ACM Trans. Inf. Syst., 22 (2004), pp. 5–53.
[5] V. Schickel-Zuber, B. Faltings, “A semantic similarity
function based on hierarchical ontologies”, in Proc.
IJCAI'07, pp. 551–556.
[6] Y. Koren, “Collaborative filtering with temporal
dynamics”, in Proc. KDD'09, pp. 447–456.
[7] A. Meena, T.V. Prabhakar, “Sentence level sentiment
analysis in the presence of conjuncts using linguistic
analysis”, in Proc. ECIR'07, pp. 573–580.
[8] H. Kanayama, T. Nasukawa, “Fully automatic lexicon
expansion for domain-oriented sentiment analysis”, in
Proc. EMNLP'06, pp. 355–363.
[9] R. Valitutti, “WordNet-Affect: an affective extension of
Wordnet” ,in Proc. International Conference on
Language Resources and Evaluation, pp. 1083–1086.
[10] B. Sarwar, G. Karypis, J. Konstan, J. Reidl,” Item-based
collaborative filtering recommendation algorithms”, in
Proc. WWW'01, pp. 285–295.