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A heterogeneous hidden Markov model for mobile app recommendation

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Abstract

Recent proliferation of ubiquitous smart phones has led to the emergence of a wide variety of apps. Selecting apps through keyword search or recommendations from friends or social networks (e.g., Facebook) may not match the real preferences of users, especially when the need is just-in-time and context specific. Although there are many collaborative filtering approaches that are capable of generating time-aware recommendations, most of them work on modeling of the time stamps (the time that events happen) rather than modeling of the sequential patterns (in cases that time stamps are not available) as well as investigating the factors behind those patterns. In this paper, we propose a mechanism for modeling three important factors governing the app installation of smart phone users: (1) short-term context, (2) co-installation pattern, and (3) random choice. Specifically, we use a hidden Markov model equipped with heterogeneous emission distributions to incorporate these factors. Apps being installed are probabilistically categorized into one of these factors, and app recommendations for users are carried out accordingly. This coherent model can be inferred effectively by using Gibbs sampling. The formulation has a significant advantage that the performance is less sensitive to data sparsity and incompleteness. Empirical results show that it has higher performance in recommending mobile apps to smart phone users, measured in terms of precision and area under the ROC curve (AUC). Besides, the proposed model allows the nature of the apps, with respect to the three factors, to be revealed as well as the extent to which each user is affected by the three factors to be inferred, providing additional insights on the users’ behavior.

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Notes

  1. The hidden state for marking sequence boundaries serves as the initial state and the termination state for the HMM.

  2. For this app installation data set, we found that the time-weighted user-based CF has poorer performance than the user-based CF in most cases whenever the decay factor is greater than zero. Nevertheless, for the sake of comparison and illustration, we set it to 0.1.

  3. For the time-weighted user-based CF, the weight decay factor is multiplied to the rates.

  4. For the case where the last rating batch of a user contains less than 5 movies, we move also the immediate preceding batch to the test data set.

  5. The number of movies used in building MTM is much higher than that for mobile app recommendation. It is because the users rated the movies in batch and the order of rating the movies within a batch does not necessarily follow the order of exploring the movies. Thus, the range of dependence among movies in the sequence is much longer.

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Acknowledgements

This work is partially supported by HKBU Faculty Research Grant FRG2/13-14/050.

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Authors and Affiliations

  1. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Victor C. Cheng, Li Chen, William K. Cheung & Chi-kuen Fok

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  1. Victor C. Cheng
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  2. Li Chen
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  3. William K. Cheung
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  4. Chi-kuen Fok
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Correspondence to Victor C. Cheng.

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Cheng, V.C., Chen, L., Cheung, W.K. et al. A heterogeneous hidden Markov model for mobile app recommendation. Knowl Inf Syst 57, 207–228 (2018). https://doi.org/10.1007/s10115-017-1124-3

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  • DOI: https://doi.org/10.1007/s10115-017-1124-3

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