A Hybrid Grey Wolf Optimizer-Tabu Search Approach for Multi-Day Tourist Itinerary Recommendation Based on Content-Based Filtering

Abstract

Planning a multi-day city trip is a hard combinatorial problem: a tourist must choose which attractions to visit, in what order, and on which day, while respecting opening hours, travel time, a daily time budget, and a personal preference and budget profile. The authors present an end-to-end recommendation system for Jakarta that couples a content-based filtering stage with a route optimization stage cast as a Tourist Trip Design Problem (TTDP). The first stage scores candidate attractions using TF-IDF similarity, a Bayesian weighted rating, a category-based budget filter, and a Maximal Marginal Relevance step for diversity. The second stage searches over visit permutations with a time-budget decoder that inserts a lunch break and treats closing hours as a hard constraint. As the optimizer, the authors introduce a hybrid Grey Wolf Optimizer with an embedded Tabu Search local search (GWO–TS), adapted from a continuous formulation to the discrete permutation space using swap sequences. GWO–TS replaces a previously deployed genetic–particle-swarm hybrid as the default optimizer and is exposed through a REST API that powers a live web application. The dataset is built entirely from open sources like OpenStreetMap, the Massive-STEPS check-in corpus, Google Places, Wikipedia, and OSRM—yielding 166 operational attractions and 181 hotels across eight spatial clusters. The authors describe the formulation, the algorithm, and an evaluation protocol that compares GWO–TS against genetic algorithm, particle swarm optimization, and their hybrid over one-, three-, and five-day scenarios using fitness, a user satisfaction score, constraint violations, and runtime.