Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach
Vincent W. Zheng, Bin Cao, Yu Zheng, Xing
Xie and Qiang Yang (AAAI-10)
The paper “Collaborative Filtering Meets
Mobile Recommendation: A User-Centered Approach” introduces an approach,
user-centered collaborative location and activity filtering (UCLAF), to extract
data from many users and apply collaborative filtering to find like-minded
users and like-patterned activities at different locations. These findings from
social information can help to do mobile recommendation on location and
activities. Different from previous work, Vincent et al. model the
user-location activity relations with a tensor representation; and suggest that
a regularized tensor and matrix decomposition are better to address the sparse
data problem. The result of this paper shows that UCLAF has a better
performance compared to some state-of-the-art solutions.
With the
rapidly increase of internet data about location, location tracking services
such as GPS are very good resources which can be exploited to make timely and
targeted recommendations for users on places which users might be interested,
and activities they are likely to enjoy at a specific place. The authors aim to
mine users’ GPS trajectories based on their partial location and activity
annotations, and then build the model to recommend locations and activities to
individual users. Because of considering that activities are location-dependent,
it motivated them to build their model that help to make inferences
collaboratively rather than separately.
Vincent
et al. first extract users’ location and activities annotations and then model
them as a user-location-activity tensor which presents the information that users
visited some place and did something there. For recommendations, they use
collaborative filtering method to fill the tensor. Regularized tensor and
matrix decomposition are applied for collaborative filtering solution. The
tensor is decomposed into some low dimensional matrices with respect to tensor
entity (i.e., users, locations and activities):
- User-user matrix: encodes the user-user similarities in a social network
- Location-feature matrix: each feature refers to the number of point of interests
- Activity-activity matrix: represent the activity-activity correlations
- User-location matrix: extracted from GPS data to model the user-location visiting relations in the case when a user visited some place but we don’t have information about what she was doing there.
After
these low-dimensional representations are obtained, they reconstruct the tensor
by filling all the missing entries in the tensor so that they have enough
information to do recommendations for all users.
For experiments, the authors collected
data from 164 users who carried GPS devices to record their trajectories from
April 2007 5o Oct. 2009 in Beijing, China. The dataset consists of 12,765 GPS
trajectories with a total length over 139,310 kilometers. They consider 5
different types of activities including “Food& Drink”, “Shopping”, “Movies
& Shows”, “Sports & Exercise” and “Tourism & Amusement”. After
clustering, they obtained 168 locations, 164 users, 5 activities and 14
location features.
To evaluate their system, Vincent et al.
employ 5 baselines for comparison: user-based CF (UCF), location-based CF
(LCF), activity-based CF (ACF), unifying user-location-activity CF (ULA),
high-order singular value decomposition (HOSVD). They used two metrics:
- RMSE (root mean square error): measure the tensor reconstruction loss on the hold-out test data
- nDCG (normalized discounted cumulative gain): measure the ranking results of our retrieved location/activity list
The result from evaluating the system on a
GPS dataset shows 19% improvement on location recommendation, and 22%
improvement on activity recommendation over the simple memory-based CF baseline
(i.e. UCF, LCF, ACF).
In the scope of this paper, we can see that leveraging social information access helps to improve recommender systems’ performances; especially, in the cases the data of users is sparse. Collaborative filtering techniques are widely applied by gathering data from many users and online resources to find like-minded users and like-patterned activities. The proposed approach of Vincent et al. demonstrated the usefulness of social information. In particular, the paper shows how to answer two common questions in location-activity recommender systems: “If I want to do something, where should I go?” and “If I will visit some place, what can I do there?”.
In the scope of this paper, we can see that leveraging social information access helps to improve recommender systems’ performances; especially, in the cases the data of users is sparse. Collaborative filtering techniques are widely applied by gathering data from many users and online resources to find like-minded users and like-patterned activities. The proposed approach of Vincent et al. demonstrated the usefulness of social information. In particular, the paper shows how to answer two common questions in location-activity recommender systems: “If I want to do something, where should I go?” and “If I will visit some place, what can I do there?”.
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