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Context relevance assessment and exploitation in mobile recommender systems

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Abstract

In order to generate relevant recommendations, a context-aware recommender system (CARS) not only makes use of user preferences, but also exploits information about the specific contextual situation in which the recommended item will be consumed. For instance, when recommending a holiday destination, a CARS could take into account whether the trip will happen in summer or winter. It is unclear, however, which contextual factors are important and to which degree they influence user ratings. A large amount of data and complex context-aware predictive models must be exploited to understand these relationships. In this paper, we take a new approach for assessing and modeling the relationship between contextual factors and item ratings. Rather than using the traditional approach to data collection, where recommendations are rated with respect to real situations as participants go about their lives as normal, we simulate contextual situations to more easily capture data regarding how the context influences user ratings. To this end, we have designed a methodology whereby users are asked to judge whether a contextual factor (e.g., season) influences the rating given a certain contextual condition (e.g., season is summer). Based on the analyses of these data, we built a context-aware mobile recommender system that utilizes the contextual factors shown to be important. In a subsequent user evaluation, this system was preferred to a similar variant that did not exploit contextual information.

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Notes

  1. For the ratings we assume a normal distribution; therefore the t test is appropriate to detect significant deviations of MCY from MCN.

  2. In fact, as we will see later on, it is constant with respect to the number of input data and linear in the number of contextual conditions.

  3. http://www.netflixprize.com.

  4. http://www.movielens.org.

  5. We note that this is actually a big advantage for the proposed recommender, as it does not suffer from the “new user problem”, i.e., the impossibility to deliver a recommendation to a user new to the system, i.e., that has not entered yet any rating [32].

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Authors and Affiliations

  1. Free University of Bolzano, Piazza Domenicani, 3, 39100, Bozen-Bolzano, Italy

    Linas Baltrunas, Bernd Ludwig, Stefan Peer & Francesco Ricci

Authors
  1. Linas Baltrunas
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  2. Bernd Ludwig
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  3. Stefan Peer
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  4. Francesco Ricci
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Corresponding author

Correspondence to Francesco Ricci.

Appendices

Appendix 1: User preferences for categories of points of interest

In phase 1 of our study on the relevance of contextual factors (see Sect. 2), we measured the relevance of a contextual factor by the normalized mutual information between the response of the user and each contextual factor: the higher the mutual information, the better the contextual factor can explain the response of the user to the questions in the interviews. In the Table 2 to follow, we present an overview of the contextual factors ordered by different POI category:

Table 2 An overview of the contextual factors ordered by different POI category
Full size table

Appendix 2: ratings for points of interest in different contexts

The Table 3 in this section presents a comparison between ratings of POI in Bolzano without any context and ratings of the same items assuming a certain contextual condition to hold. In order to keep the sampling of the necessary probability distributions by means of the user study in Sect. 3 tractable and to stay in line with the linear predication model for collaborative filtering, we assume the contextual factors to be independent:

$$ P(R|C_1,\ldots,C_k)\approx \prod_{i=1}^k P(R|C_i) $$

In order to find out which contextual condition C i where considered as relevant for their context-dependent rating of POI, we compare P(R|C i ) to P(R) with the help of a t test. With this test, we determine the contextual conditions that induce a statistically significant difference on the average rating of the POIs of a certain category:

Table 3 The influence of contextual conditions on ratings for POI in Bolzano
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Baltrunas, L., Ludwig, B., Peer, S. et al. Context relevance assessment and exploitation in mobile recommender systems. Pers Ubiquit Comput 16, 507–526 (2012). https://doi.org/10.1007/s00779-011-0417-x

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