VEGAR: A tourist attraction recommender system based on signed feedback and a signed spatial-sentiment knowledge graph

Tourism has grown rapidly. Travelers face abundant attractions and heterogeneous online content. This increases the cognitive effort of trip planning and motivates personalized recommendation. Existing recommender systems emphasize objective features but they exploit subjective review signals superficially, overlooking user personality cues and attraction-level signed sentiment. They also tend to treat users’ visits as uniformly positive feedback, even though negative preferences are common and informative in textual reviews. To address these issues, we propose VEGAR, a tourist attraction recommender system that jointly models signed feedback within a signed spatial-sentiment knowledge graph. To exploit subjective review signals, VEGAR introduces a subjective feature extraction module and designs a signed spatial-sentiment knowledge graph construction framework. The resulting signed spatial-sentiment knowledge graph organizes both individual user personality and signed collective inclinations toward attractions in three subgraphs. To model feedback polarity and align it with signed sentiment, VEGAR proposes a prediction module with five propagation procedures over three subgraphs to learn signed user and attraction preference profiles. VEGAR further introduces high-order cross aggregation module and gated fusion to achieve informative user and attraction embeddings. To capture spatial constraints while preserving semantic relevance, VEGAR proposes spatial-sentiment attention that jointly models semantic relevance and distance-based influence during propagation. Experiments on the Suzhou Sina Weibo dataset show that VEGAR achieves average relative improvements of 2.85% in AUC and 2.80% in F1 score over all compared baselines. VEGAR also achieves the best Top-K ranking performance.