Journal
KNOWLEDGE-BASED SYSTEMS
Volume 233, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2021.107521
Keywords
Recommendation system; Travel recommendation; Keywords generation; Deep learning; Multi-task learning
Categories
Funding
- 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 [72172057, 71701089]
- Jiangsu Provincial Key Re-search and Development Program, China [BE2020001-3]
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This study introduces a TRKG model that provides a more comprehensive product representation and accurate recommendations by jointly modeling click sequences and product keywords in the title. Significant improvements were achieved in both travel recommendation and keyword generation tasks compared to existing methods.
Travel recommendation is very critical to helping users quickly find products or services that they are interested in. The key to travel recommender systems is learning user shopping intentions, which are expressed through various supervision signals, such as the clicked products and their titles. Existing travel recommendation methods commonly infer user intentions from click behaviors on travel products. However, remarkable keywords in the product title, such as departure, destination, travel time, hotel, and transportation are paid less attention. To this end, we hypothesize that modeling click sequences and product keywords in title jointly would result in a more holistic representation of a product and towards more accurate recommendations. Thus, we propose a TRKG (short for Travel Recommendation with Keywords Generation) model, which fulfills the travel recommendation and keywords generation tasks simultaneously. To generate explainable outputs, unlike most previous approaches that regard the product title as a hidden feature vector, TRKG regards keywords in the product title as an additional supervision signal. Meanwhile, TRKG integrates the long-term and short-term user preferences in the travel recommendation component and the keywords generation component. To evaluate the proposed model, we constructed datasets from a large tourism e-commerce website in China. Extensive experiments demonstrate that the proposed method yields significant improvements over state-of-the-art methods. (C) 2021 Elsevier B.V. All rights reserved.
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