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Exploring timeliness for accurate recommendation in location-based social networks

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  • An individual's location history in the real world implies his or her interests and behaviors. This paper analyzes and understands the process of Collaborative Filtering (CF) approach, which mines an individual's preference from his/her geographic location histories and recommends locations based on the similarities between the user and others. We find that a CF-based recommendation process can be summarized as a sequence of multiplications between a transition matrix and visited-location matrix. The transition matrix is usually approximated by the user's interest matrix that reflect the similarity among users, regarding to their interest in visiting different locations. The visited-location matrix provides the history of visited locations of all users, which is currently available to the recommendation system. We find that recommendation results will converge if and only if the transition matrix remains unchanged; otherwise, the recommendations will be valid for only a certain period of time. Based on our analysis, a novel location-based accurate recommendation (LAR) method is proposed, which considers the semantic meaning and category information of locations, as well as the timeliness of recommending results, to make accurate recommendations. We evaluated the precision and recall rates of LAR, using a large-scale real-world data set collected from Brightkite. Evaluation results confirm that LAR offers more accurate recommendations, comparing to the state-of-art approaches.

    Mathematics Subject Classification: Primary: 68T20, 68T37; Secondary: 68U01.


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  • Figure 1.  Overview a location-based social network

    Figure 2.  Influence of training size to the precision

    Figure 3.  Cosine similarity between the transition and similarity matrix

    Figure 4.  Average number of new visited location changes through each month

    Figure 5.  Cumulative distribution function (CDF) of time interval between timelinesss

    Figure 6.  Precision and recall of CF with or without K-means clustering and SVD

    Figure 7.  Influence of data sparsity and category to the recommendation rate (recall ratio) where number of recommendations $k = 20$

    Figure 8.  Influence of constant and dynamic similarity to the recommendation rate (precision ratio) where $N$ is number of multiplications

    Figure 9.  Recommendation timeliness comparison for constant similarity matrix and dynamic similarity matrix

    Figure 10.  Similarity of the eigenvector of transition matrix and similarity matrix comparisons of our method and the three benchmarks

    Figure 11.  The recommendation rate of our method and the three baseline varying in the recommendation timeliness (month)

    Figure 12.  The empirical CDF of time intervals of our method and the three benchmarks except CF varying in the recommendation period time (month)

    Figure 13.  Precisions and recalls of LAR, CF, LGS and GeoSoCa

    Table 1.  An example of user-item matrix

    Apple Pear Grape Watermelon
    Alice Like Like Dislike Dislike
    Bob Dislike Like Like
    Chris Dislike Like
    Tony Like Dislike
     | Show Table
    DownLoad: CSV

    Table 2.  A summary of precision and recall ratios of the four approaches

    Models Precision Recall Avg.Precision Avg.Recall
    CF 0.51 0.69 0.3573 0.6395
    LGS 0.59 0.77 0.3682 0.6762
    GeoSoCa 0.58 0.76 0.3657 0.6684
    LAR 0.62 0.83 0.4582 0.7523
     | Show Table
    DownLoad: CSV
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