Résumé:
In this graduation thesis, we focused on exploring novel methods and approaches in data anal-
ysis and processing for recommendation systems. We aimed to address the challenges posed
by diverse and heterogeneous data to contribute to the advancement of recommendation sys-
tems. By effectively analyzing and processing such data, we can unlock the true potential of
recommendation systems, enabling users to make better decisions and discover new experiences.
To build our recommendation system, we established models for points of interest (POIs)
and users. Our solution incorporated three key factors: sentiment analysis, user preferences,
and ratings, culminating in the integration of the lightGCN model. Preprocessing and filtering
of the data were performed to ensure data quality, followed by modelling the POIs based on
their unique characteristics. The sentiment analysis factor played a crucial role in analyzing
user reviews and predicting ratings. By employing sentiment analysis techniques, we accurately
represented the user’s opinion by aligning the sentiment expressed in textual reviews with the
user’s rating. The user preference factor enabled us to recommend the most suitable POIs
based on individual preferences and interests. The rating factor, which examined the ratings
users gave to visited POIs, facilitated tracking and updating user preferences. This allowed
for dynamic adjustment and refinement of the user preference profile, ensuring recommenda-
tions aligned with their evolving interests. The culmination of these factors, along with the
preprocessing, filtering, and modelling of the POIs, led to the integration of the lightGCN
model. By combining similarity scores derived from the user preference profile, sentiment anal-
ysis score, and ratings score, the lightGCN model predicted the most suitable POIs for each
user, enhancing the recommendation system’s accuracy.
During the experimentation phase, we utilized Yelp datasets in a Jupyter environment to
preprocess, filter, and model the POIs, incorporating sentiment analysis of reviews. The rec-
ommendation system, developed in the same environment, utilized the combined results of the
three factors and the lightGCN model to provide improved POI recommendations for users
