期刊
KNOWLEDGE AND INFORMATION SYSTEMS
卷 63, 期 7, 页码 1687-1716出版社
SPRINGER LONDON LTD
DOI: 10.1007/s10115-021-01567-3
关键词
Itinerary recommendation; Group itinerary recommendation; Deep learning; Attention mechanism; Iterated local search
资金
- National Key Research and Development Program of China [2017YFD0401001]
- Key Program of National Natural Science Foundation of China [92046026]
- National Natural Science Foundation of China [71701089]
- Jiangsu Provincial Key Research and Development Program, China [BE2020001-3]
- jiangsu Provincial Policy Guidance Program, China [BZ2020008]
This paper proposes an AMT-IRE framework, which dynamically learns the inner relations between group members and obtains consensus group preferences via the attention mechanism. By integrating POI categories and textual information, combined with attention networks, group itineraries are recommended effectively.
Tourism is one of the largest service industries and a popular leisure activity participated by people with friends or family. A significant problem faced by the tourists is how to plan sequences of points of interest (POIs) that maintain a balance between the group preferences and the given temporal and spatial constraints. Most traditional group itinerary recommendation methods adopt predefined preference aggregate strategies without considering the group members' distinctive characteristics and inner relations. Besides, POI textual information is beneficial to capture overall group preferences but is rarely considered. With these concerns in mind, this paper proposes an AMT-IRE (short for Attentive Multi-Task learning-based group Itinerary REcommendation) framework, which can dynamically learn the inner relations between group members and obtain consensus group preferences via the attention mechanism. Meanwhile, AMT-IRE integrates POI categories and POI textual information via another attention network. Finally, the group preferences are used in a variant of the orienteering problem to recommend group itineraries. Extensive experiments on six datasets validate the effectiveness of AMT-IRE.
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