Context has been recognized as an important factor to consider in personalized recommender systems. Particularly in location-based services (LBSs), a fundamental task is to recommend to a mobile user where he/she could be interested to visit next at the right time. Additionally, location-based social networks (LBSNs) allow users to share location-embedded information with friends who often co-occur in the same or nearby points-of-interest (POIs) or share similar POI visiting histories, due to the social homophily theory and Tobler’s first law of geography. So, both the time information and LBSN friendship relations should be utilized for POI recommendation.
Tensor completion has recently gained some attention in time-aware recommender systems. The problem decomposes a user-item-time tensor into low-rank embedding matrices of users, items and times using its observed entries, so that the underlying low-rank subspace structure can be tracked to fill the missing entries for time-aware recommendation. However, these tensor completion methods ignore the social-spatial context information available in LBSNs, which is important for POI recommendation since people tend to share their preferences with their friends, and near things are more related than distant things. In this paper, we utilize the side information of social networks and POI locations to enhance the tensor completion model paradigm for more effective time-aware POI recommendation. Specifically, we propose a regularization loss head based on a novel social Hausdorff distance function to optimize the reconstructed tensor. We also quantify the popularity of different POIs with location entropy to prevent very popular POIs from being over-represented hence suppressing the appearance of other more diverse POIs. To address the sensitivity of negative sampling, we train the model on the whole data by treating all unlabeled entries in the observed tensor as negative, and rewriting the loss function in a smart way to reduce the computational cost. Through extensive experiments on real datasets, we demonstrate the superiority of our model over state-of-the-art tensor completion methods.
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Adversarial Point-of-Interest Recommendation
Point-of-interest (POI) recommendation is essential to a variety
of services for both users and business. An extensive number of
models have been developed to improve the recommendation performance
by exploiting various characteristics and relations among
POIs (e.g., spatio-temporal, social, etc.). However, very few studies
closely look into the underlying mechanism accounting for why
users prefer certain POIs to others. In this work, we initiate the
first attempt to learn the distribution of user latent preference by
proposing an Adversarial POI Recommendation (APOIR) model, consisting
of two major components: (1) the recommender (R) which
suggests POIs based on the learned distribution by maximizing the
probabilities that these POIs are predicted as unvisited and potentially
interested; and (2) the discriminator (D) which distinguishes
the recommended POIs from the true check-ins and provides gradients
as the guidance to improve R in a rewarding framework. Two
components are co-trained by playing a minimax game towards improving
itself while pushing the other to the boundary. By further
integrating geographical and social relations among POIs into the
reward function as well as optimizing R in a reinforcement learning
manner, APOIR obtains significant performance improvement in
four standard metrics compared to the state of the art methods.
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- NSF-PAR ID:
- 10122595
- Date Published:
- Journal Name:
- The World Wide Web Conference, {WWW} 2019, San Francisco, CA, USA, May 13-17, 2019
- Page Range / eLocation ID:
- 3462 to 34618
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3308558.3313609
