Abstract:
"The storage capacity of LBSN's have expanded mainly as result of an upsurge in visits and
reviews. The prospective usefulness of such information in recommendation prompted the
initiation of point of interest recommendation solutions development. The original
solutions which were implemented began to incorporate a few primary important aspects,
namely social, geological, content, and spatial, which have a bearing upon the suggestion of
points of interest. Subsequently, these aspects integrated with contemporary recommendation
methodologies such as collaborative filtering and content-based filtering. Nevertheless, current
studies indicate that the utilization of media in social networking platforms has emerged as a
promising avenue for capturing user engagement. Although the analysis of media constitutes a
noteworthy effort in determining user preferences, its significance may be rendered futile in
the absence of linguistic context. The study method has resulted in the identification of a gap.
The method suggested in this document aims to bridge the existing gap by integrating linguistic
context and image classification techniques to effectively deliver personalized suggestions.
The suggested method aims to utilize social networking platforms as a foundation for
identifying user preferences through the collection of likes on those platforms. The favorited
content are divided into two components: media and text. The content will undergo text
processing techniques such as sentiment detection, semantic evaluation and POS tagging to
ascertain its intended meaning and the individual's subjective evaluation of it. The media will
undergo classification in order to determine their respective representations. The findings are
aggregated to enhance the precision and dependability of suggestions by offering a significant
framework to the subject matter. The individual's preferences are subsequently inputted
through a rating algorithm, which determines the most significant topics for the user. The
algorithm will utilize these preferences to classify the locations it recommends to the
individual."
