Trilateral Spatiotemporal Attention Network for User Behavior Modeling in Location-based Search

Qi, Yi; Hu, Ke; Zhang, Bo; Cheng, Jia; Lei, Jun

doi:10.1145/3459637.3482206

Cited by 6 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, on the basis of the assumption that users with similar behaviors have similar preferences on stores, traditional Collaborative Filtering (CF) [6] has been extensively applied in modern takeaway recommender systems. Since CF-based methods [10,14,25] suffer from data sparsity and cold start issues, many works tried to utilize additional information such as time information [16,23,31,38], spatial information [16,23,38] and attributes [16,31,40] to enhance recommendation accuracy. For example, DAT [40] enriched the user and store representations based on the corresponding user features and store attribute information for the recommendation.…”

Section: Related Work 21 Takeaway Recommendationmentioning

confidence: 99%

“…For example, DAT [40] enriched the user and store representations based on the corresponding user features and store attribute information for the recommendation. Moreover, TRISAN [23] adopted an attention-based fusion mechanism to capture the rich interplay between spatial information and time information to improve recommendation performance. Further, considering the importance of time information in takeaway systems, some methods attempted to not simply utilize the time information as the auxiliary input of the designed model.…”

Section: Related Work 21 Takeaway Recommendationmentioning

confidence: 99%

“…Compared with traditional recommendation, takeaway recommendation appears greatly more difficult due to the complicated relationships among users, stores and foods. Thus, takeaway recommender systems have attracted great attention from the research community and industry field [16,23,31,38,40].…”

Section: Introductionmentioning

confidence: 99%

“…Existing takeaway recommendation methods commonly leverage the coarse-grained single interaction data between the user and store or food and may further utilize the time as auxiliary information [16,23,38,40]. With feature-enriched user and store representations as input, [40] utilized the classical two-tower model to make store recommendations based on the historical interaction data between users and stores.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modeling Dual Period-Varying Preferences for Takeaway Recommendation

Zhang,

Wu,

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

Takeaway recommender systems, which aim to accurately provide stores that offer foods meeting users' interests, have served billions of users in our daily life. Different from traditional recommendation, takeaway recommendation faces two main challenges:(1) Dual Interaction-Aware Preference Modeling. Traditional recommendation commonly focuses on users' single preferences for items while takeaway recommendation needs to comprehensively consider users' dual preferences for stores and foods. (2) Period-Varying Preference Modeling. Conventional recommendation generally models continuous changes in users' preferences from a session-level or day-level perspective. However, in practical takeaway systems, users' preferences vary significantly during the morning, noon, night, and late night periods of the day. To address these challenges, we propose a Dual Period-Varying Preference modeling (DPVP) for takeaway recommendation. Specifically, we design a dual interaction-aware module, aiming to capture users'

show abstract

Section: Related Work 21 Takeaway Recommendationmentioning

confidence: 99%

Section: Related Work 21 Takeaway Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling Dual Period-Varying Preferences for Takeaway Recommendation

Zhang,

Wu,

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

show abstract

“…SLi-Rec [30] models user long-term and shortterm interests by introducing time-aware and content-aware controllers. TRISAN [23] leverages a triangular relationship between user geographic location, item geographic location, and user click time to enhance the representation of spatial-temporal information. BASM [7] proposes a bottom-up network to model multiple spatialtemporal data distributions.…”

Section: Related Workmentioning

confidence: 99%