A personalised malayalam travel recommendation model using deep clustering techniques.

Abstract

The research aims to develop a Personalized Travel Recommender System tailored for the Malayalam-speaking audience in Kerala, India. Due to the unavailability of a benchmark dataset in Malayalam, a two-pronged data collection strategy was employed: extraction of 13458 travelogues and reviews from Facebook's largest travel group in Kerala, 'Sanchari’, and independent travel blogs and collecting 2,006 records via a Google form. As part of this work, firstly, it seeks to develop an automated framework for processing Malayalam text scraped from social media, addressing the language's rich morphological complexity. Second, it intends to create an intelligent system that utilizes opinion mining techniques to continuously learn user preferences, thereby enabling highly personalized travel suggestions. Finally, the work aims to design a recommender model that leverages machine learning algorithms to identify tourist destinations tailored to the preferences of travelers who exhibit similar tastes, employing both collaborative and content-based filtering techniques. Data collection process was the biggest hurdle in the initial phase. Collecting Malayalam lengthy travelogue was the aim to create a dataset. The focus reached the Facebook group named sanchari, which is the largest travel group in Malayalam Language. Data collection faced several challenges such as memory leak, bot detection, performance optimisation and page rendering time. By utilizing some special features exclusively available for group admins utilized to solve these issues. All travelogues written in English, Manglish or any language other than Malayalam removed from the spreadsheet before preprocessing. To transform this unstructured data into a usable dataset, a variety of Natural Language Processing techniques, along with a Part of Travelogue Tagger (POT Tagger) and Look-up Dictionary, were used for preprocessing. Feature engineering included vectorization and one-hot encoding of important variables to create 'Travel DNA' and 'Location DNA.' Travel DNA is composed by aggregating key travel attributes such as travel type, travel mode, and user preferences into a numerical vector through techniques like one-hot encoding and vectorization, thereby providing a compact representation of travel patterns of users. Location DNA is composed by gathering essential characteristics of various travel destinations, such as location type, climate, and popularity, and converting them into a numerical vector using methods like one-hot encoding and vectorization. Four distinct recommender models were developed leveraging various algorithms in Artificial Intelligence: Rule-based Cosine Similarity, Collaborative Filtering based on K-Means Clustering, Content-based Filtering through Hierarchical Agglomerative Clustering, and a model utilizing Bidirectional Long Short-Term Memory (BiLSTM) networks. Additionally, a comparative model was designed that combined autoencoders with five different machine learning algorithms. These models underwent rigorous individual testing to evaluate their performance. The rule-based cosine similarity recommender model utilizes the angle between user and item vectors in a multi-dimensional space to measure similarity, thereby providing personalized travel suggestions based on pre-defined rules and user preferences. The clustering techniques employed in this research include K-Means for collaborative filtering to group similar users, and Hierarchical Agglomerative Clustering for content- based filtering to categorize travel destinations. The BiLSTM recommender model leverages neural networks to capture both past and future context in the data. The autoencoder-based travel recommender model employs neural network architectures to compress and reconstruct the user-item interaction data, effectively capturing latent features that are used for generating more accurate and personalized travel suggestions. The RS designed with various techniques to identify the best suggestions to the users. From these models, collaborative filtering using K-Means Clustering and the model designed with Autoencoder exhibit promising results.