Enhancing personalized trip recommendations with attractive route analysis and graph attention auto-encoder

Personalized trip recommendations aim to offer an itinerary featuring various points of interest (POIs) to the user. Many previous works search POIs only according to their popularity. However, the routes between the POIs are attractive to visitors, and some of these routes are very popular. This kind of route, which enhances the user experience, is referred to as AR. In this paper, we investigate attractive routes in order to enhance personalized trip recommendation. We introduce TRAR, a personalized underlineTrip Recommender with POIs and Attractive Routes, which is comprised of three components: AR discovery, AR evaluation, and trip recommendation. We propose two methods for AR discovery: one focuses on discovering AR by analyzing the Gini coefficient and the popularity of POIs, the other is to discover AR with the help of graph attention auto-encoder (GATE). In order to discover more attractive routes for users to improve their user experience, we take the structure information of a travel graph into consideration to extract the features of routes; then we introduce GATE to AR discovery. In the AR evaluation, we estimate attractive routes’ rating scores and preferences by applying the gravity model in a category space. To enhance user experience, TRAR balances the trade-off between user experience and time cost by recommending trips that include attractive routes. The experimental results indicate that the proposed TRAR is superior to other state-of-the-art algorithms.