{"id":5862,"date":"2025-02-13T13:06:10","date_gmt":"2025-02-13T12:06:10","guid":{"rendered":"https:\/\/dbis.rwth-aachen.de\/dbis\/?p=5862"},"modified":"2025-02-13T13:13:30","modified_gmt":"2025-02-13T12:13:30","slug":"knowledge-graph-based-chinese-tourism-recommendation-system-with-large-language-models","status":"publish","type":"post","link":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/2025\/knowledge-graph-based-chinese-tourism-recommendation-system-with-large-language-models\/","title":{"rendered":"Knowledge Graph-Based Chinese Tourism Recommendation System with Large Language Models"},"content":{"rendered":"\n<p>With the increasing popularity of personalized tourism experiences, recommendation systems play a crucial role in helping travelers discover destinations, activities, and itineraries that match their preferences. Traditional recommendation models often rely on collaborative filtering or content-based filtering, which may struggle with cold-start issues, lack of contextual awareness, and limited adaptability to dynamic tourism trends.<\/p>\n\n\n\n<p>Knowledge graphs (KGs) provide structured representations of entities and their relationships, offering a powerful way to enhance recommendation systems by integrating domain knowledge, user-generated content, and external tourism data. Additionally, Large Language Models (LLMs) can be used to enrich the KG with semantic understanding, improving recommendations by leveraging context-aware reasoning and natural language interactions.<\/p>\n\n\n\n<p>This thesis explores the development of a knowledge graph-based tourism recommendation system for Chinese travelers by constructing a time-sensitive ontology, integrating tourism data from Xiaohongshu (Red Notes), and leveraging LLMs for enhanced personalization and recommendation generation.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>With the increasing popularity of personalized tourism experiences, recommendation systems play a crucial role in helping travelers discover destinations, activities, and itineraries that match their preferences. Traditional recommendation models often rely on collaborative filtering or content-based filtering, which may struggle with cold-start issues, lack of contextual awareness, and limited adaptability to dynamic tourism trends. Knowledge [&hellip;]<\/p>\n","protected":false},"author":19,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[21],"tags":[],"class_list":["post-5862","post","type-post","status-publish","format-standard","hentry","category-thesis"],"acf":[],"_links":{"self":[{"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/posts\/5862","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/comments?post=5862"}],"version-history":[{"count":2,"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/posts\/5862\/revisions"}],"predecessor-version":[{"id":5865,"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/posts\/5862\/revisions\/5865"}],"wp:attachment":[{"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/media?parent=5862"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/categories?post=5862"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dbis.rwth-aachen.de\/dbis\/index.php\/wp-json\/wp\/v2\/tags?post=5862"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}