Abstract:
Emotion-based recommendation is widely used in many recommendation domains
and has been discussed in context-aware recommendation. Adapting emotion is
mainly followed by the notion of the different role of emotion in the process of
recommendation as users interact with the system. Ample researches have been
suggested in the domains of music and movie recommendation but very few studies
are found on travel recommendation. Thus, the proposed study used emotion and
user behavior as contexts and used pre-filtering and contextual modeling
approaches to find the effectiveness of travel destination recommendations. In the
filtering approach, the system recommendation was implemented in contextual
pre-filtering paradigm and used the contextual information to select the most
relevant item and user data for generating recommendations by using item-item
collaborative filtering. Top five destinations were generated as recommendations
to trace the effectiveness of the recommendation by using Loglikelihood similarity
and Simple Weighted Average predictive rating calculation algorithm. In contextual
modeling, CANDECOMP/PARAFAC(CP) Tensor Factorization model was adapted,
which used ratings from M users for N items under Q types of contexts as a threedimensional tensor and generated the top five recommendations for each context.
As the contexts, both emotion and user behavior details were incorporated in
recommendation engine for the comparison. A new corpus with emotion context for
place recommendation was developed by using Semantic Analysis techniques due
to the lack of properly recorded dataset and the derived dataset used in the
implementation. In the process of deriving emotion tags, we used the text reviews
collected from TripAdvisor and defined an emotion tag for each selected destination
based on lexicon-based semantic classification. Both approaches with contexts
outperformed with the selected contextual parameters and results of tensor
factorization approach with user emotion and user behavior proved higher
effectivity in tourist destination recommendation compared to other approaches
(Mean Average Precision = 81.59 %). Our study focused on a challenging field, such
as tourist destination recommendation while selecting the emotion and user
behavior as contextual parameters and the selected contextual parameters proved
user satisfactions towards the recommendation generated by the system.
