Recommendation Systems
Advanced algorithms for personalized walking route and destination suggestions
Advanced algorithms for personalized walking route and destination suggestions
Recommendation systems in walking applications represent a crucial intersection of geospatial computing, user preference modelling, and route optimisation. These systems aim to suggest relevant walking routes, destinations, and points of interest based on user behaviour, contextual factors, and environmental conditions.
Unlike traditional recommendation systems that deal with discrete items, walking recommendation systems must consider spatial relationships, temporal dynamics, and the physical constraints of pedestrian movement through geographic space.
Collaborative filtering in walking applications leverages the collective behaviour and preferences of users to make recommendations. User-based approaches identify individuals with similar walking patterns and preferences to recommend routes taken by like-minded walkers. Item-based collaborative filtering recommends routes or destinations based on similarity to previously enjoyed locations, whilst matrix factorisation techniques such as Non-negative Matrix Factorisation (NMF) or Singular Value Decomposition (SVD) discover latent factors in user-route interactions.
Content-based approaches focus on the intrinsic properties of walking routes and destinations. Route characteristics include distance, elevation gain, surface type, scenic value, and safety ratings. Environmental features encompass the presence of parks, water bodies, historical landmarks, and accessibility features. Temporal attributes account for seasonal variations, time-of-day preferences, and weather dependencies, whilst user profile matching aligns route characteristics with individual preferences for difficulty, duration, and interests.
Most successful walking recommendation systems combine multiple approaches through various hybridisation strategies. Weighted hybridisation combines collaborative and content-based scores with learned or fixed weights, whilst switching hybridisation uses different recommendation strategies based on data availability or user context. Mixed hybridisation presents recommendations from multiple algorithms simultaneously, and feature combination uses collaborative data as features in content-based models.
Effective recommendation systems depend on a rich and diverse set of data sources. User interaction data, including walking history, route completion rates, user ratings, and time spent at different locations, provides the behavioural insights necessary for personalisation. This is complemented by geographic data, which encompasses road networks, pedestrian paths, points of interest, and terrain information that define the walkable environment. Environmental data adds further context, with weather history, seasonal patterns, and safety statistics enabling more nuanced and situationally-aware recommendations. Finally, social data such as user reviews, social media check-ins, and other community-contributed content can enrich place descriptions and highlight popular or noteworthy locations.
Evaluating the performance of a walking recommendation system requires a multi-faceted approach. Accuracy metrics such as precision, recall, and the F1-score are used to assess binary relevance—whether a recommendation is good or not. Ranking metrics, including Mean Average Precision (MAP) and Normalised Discounted Cumulative Gain (NDCG), evaluate how well the system orders the most relevant results. Beyond simple accuracy, diversity measures assess the variety within recommendation lists, ensuring users are not repeatedly shown similar routes. Novelty and serendipity metrics measure the system's ability to suggest previously unexplored but relevant routes, preventing filter bubbles. Ultimately, user satisfaction, measured through survey-based feedback, route completion rates, and return usage, provides the most critical indicator of system success.
Recommendation systems must account for: