Who Will Travel With Me? Personalized Ranking Using Attributed Network Embedding for Pooling

In ride matching, the search results can be personalized for a particular driver. Given a query with trip plans, it is advantageous to rank potential riders in terms of who are most appealing to the driver for increasing occupancy rates. While personalized ranking approaches such as collaborative filtering and factorization are available, they are not suitable for pooling because candidate riders are associated with different preferences, and their travel is sparsely distributed with a long tail of users for a few popular destinations. The user embedding method is a good candidate in terms of alleviating data sparsity, but it has issues such as difficulty encoding user preferences from rich information. In this study, we explore user embedding techniques for the purposes of short-term personalized rider ranking, where the aim is to present to drivers a set of potential riders who share similar itineraries with them and can be picked up on their current route. Considering trip requests, along with the preferences issued in advance, this study uses attribute representations to rank the riders based on the higher-order similarities in the participants’ itineraries in a three-step manner: (i) start with a distributed representation of the riders’ preference regarding the cost of extra distance, (ii) generate user embeddings in a heterogeneous network with the meeting points and associated waiting times, and (iii) match and rank riders for drivers depending on an attribute fusion operation by adopting a personal route and schedule. Our proposed method performs well in an offline estimation on a huge dataset from DiDi in Chengdu, China. Experimental results indicate that with the learned embeddings, we can obtain statistically significant advancements (e.g., 4.6–29.5% increase in mean reciprocal rank (MRR); 2.8–17.4% in normalized discounted cumulative gain (nDCG)) over current methods for pooling ranking. Furthermore, we implement the proposed method on our simulated pooling system. These results validate that personalized ranking can undoubtedly boost the number of trips served, and reduce the total trip distance and waiting time.