Meta-Learned Spatial-Temporal POI Auto-Completion for the Search Engine at Baidu Maps

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Point Of Interest Auto-Completion (abbr. as POI-AC) is one of the featured functions for the search engine at Baidu Maps. It can dynamically suggest a list of POI candidates within milliseconds as a user enters each character (e.g., English, Chinese, or Pinyin character) into the search box. Ideally, a user may need to provide only one character and immediately obtain the desired POI at the top of the POI list suggested by POI-AC. In this way, the user's keystrokes can be dramatically saved, which significantly reduces the time and effort of typing, especially on mobile devices that have limited space for display and user interfaces. Despite using a user's profile and input prefixes for personalized POI suggestions, however, the state-of-the-art approach, i.e., P^3AC, still has a long way to go so as to generate not only personalized but, more importantly, time- and geography-aware suggestions. In this paper, we find that 17.9% of users tend to look for diverse POIs at different times or locations using the same prefix. This insight drives us to establish an end-to-end spatial-temporal POI-AC (abbr. as ST-PAC) module to replace P3AC at Baidu Maps. To alleviate the problem of the long-tail distribution of time- and location-specific data on POI-AC, we further propose a meta-learned ST-PAC (abbr. as MST-PAC) updated by an efficient MapReduce algorithm. MST-PAC can significantly overcome the "long-tail" issue and rapidly adapt to the cold-start POI-AC tasks with fewer examples. We sample several benchmark datasets from the large-scale search logs at Baidu Maps to assess the offline performance of MST-PAC in line with multiple metrics, including Mean Reciprocal Rank (MRR), Success Rate (SR) and normalized Discounted Cumulative Gain (nDCG). The consistent improvements on these metrics give us more confidence to launch this meta-learned POI-AC module online. As a result, the critical indicator on user satisfaction online, i.e., the average number of keystrokes in a POI-AC session, significantly decreases as well. For now, MST-PAC has already been deployed in production at Baidu Maps, handling billions of POI-AC requests every day. It confirms that MST-PAC is a practical and robust industrial solution for large-scale POI Search.