Abstract
In this paper, we propose two novel approaches for recommendation in large shopping mall scenario. For matrix factorization approach, we construct a bias matrix utilizing graph computing which fuses user’s long-term and short-term preferences. We exploit user trajectories to mine user’s frequent paths and adopt to revamping rules to update ratings from the result of matrix factorization, thus solving the problem of re-predicting customer’s preference to all shops in a new time window. For tensor decomposition approach, we add time dimension and construct a customer-shop-time three dimensional tensor, predict ratings are from the slice of the approximate tensor. We evaluate the result by top N recall and precision rate. Our data set is made on JoyCity which is a real shopping mall in Shanghai, the result is encouraging and it shows that our approach is applicative.
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Ding, Y., Wang, D., Xin, X. (2015). Novel Approaches for Shop Recommendation in Large Shopping Mall Scenario: From Matrix Factorization to Tensor Decomposition. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_42
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DOI: https://doi.org/10.1007/978-3-319-25159-2_42
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