Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts

Abstract

Many e-commerce sites present additional item recommendations to their visitors while they navigate the site, and ample evidence exists that such recommendations are valuable for both customers and providers. Academic research often focuses on the capability of recommender systems to help users discover items they presumably do not know yet and which match their long-term preference profiles. In reality, however, recommendations can be helpful for customers also for other reasons, for example, when they remind them of items they were recently interested in or when they point site visitors to items that are currently discounted. In this work, we first adopt a systematic statistical approach to analyze what makes recommendations effective in practice and then propose ways of operationalizing these insights into novel recommendation algorithms. Our data analysis is based on log data of a large e-commerce site. It shows that various factors should be considered in parallel when selecting items for recommendation, including their match with the customer’s shopping interests in the previous sessions, the general popularity of the items in the last few days, as well as information about discounts. Based on these analyses we propose a novel algorithm that combines a neighborhood-based scheme with a deep neural network to predict the relevance of items for a given shopping session.

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Notes

  1. 1.

    http://www.zalando.com.

  2. 2.

    The dataset provided to us was fully anonymized and artificially distorted so that no inference on true sales numbers on the site can be made.

  3. 3.

    Details about the used recommendation algorithm on the website were not disclosed to us.

  4. 4.

    Using much larger samples makes some of the experiments reported in later sections computationally challenging as some of the algorithms require extensive hyperparameter tuning.

  5. 5.

    The conversion rate cannot directly be interpreted as the absolute amount of additional sales that is generated by a recommender since we cannot know if an item would have been bought by a user even when it was not recommended. Previous studies however indicate that recommenders in general can turn more visitors into buyers and be effective in terms of generating additional sales (Jannach and Hegelich 2009).

  6. 6.

    Since brand loyalty is common in the fashion domain, a strong effect was generally expected.

  7. 7.

    The popularity measurement only considered view and purchases related to the items up to the time point of the recommendation on that day.

  8. 8.

    See, e.g., Manning et al. (2008) for details about these feature selection methods.

  9. 9.

    The results of the analysis for the set of 3000 occasional users are similar and are reported in “Appendix B”. The detailed results of the feature analysis are provided at http://dx.doi.org/10.17877/DE290R-18094.

  10. 10.

    The entry “Distance to first/last view” in the table refers to the time that has passed since the user has viewed a recommended item the first or last time before the purchase.

  11. 11.

    http://china-gadgets.de.

  12. 12.

    According to a four field Chi-squared test (\(p<0.05\)) with Bonferroni correction.

  13. 13.

    More information about the technical details of the algorithms are provided in Lerche et al. (2016).

  14. 14.

    We unfortunately were not yet able to benchmark C-KNN in a field study with our external partner.

  15. 15.

    The competition was organized in the context of IJCAI ’15 and TMall is a leading Chinese online market place in the style of Amazon (https://tianchi.aliyun.com/datalab/dataSet.htm?id=5).

  16. 16.

    For the sake of brevity we do not report the detailed results for the other datasets here. The additional results, which are in line with the observations for the Zalando dataset, can be found in Lerche et al. (2016).

  17. 17.

    In our empirical evaluation presented in Sect. 4.5 the baseline algorithms were used to create a set of 200 recommendations to be re-ranked, which on average led to the best results in terms of the hit rate.

  18. 18.

    In the experiments reported below we used the frequent user sample, as described in Sect. 2.1, as a basis for learning.

  19. 19.

    https://rapidminer.com.

  20. 20.

    https://h2o.ai.

  21. 21.

    The features that were not considered by DeepPredict are listed in the appendix in Table 16. All non-considered features are related to the recent popularity of a certain brand or category.

  22. 22.

    y is the correct label (1 or 0) and p the predicted probability.

  23. 23.

    As in our previous experiments, we took the first 200 recommendations of the baseline algorithms as a basis for re-ranking as this cut-off value on average led to the best results in terms of the hit rate.

  24. 24.

    Revealing fewer view items of the current session leads to accuracy values that are lower on an absolute scale; the ranking of the algorithms is however not changed.

  25. 25.

    All reported differences are statistically significant according to a Student’s t-test at \(p<0.01\) with Bonferroni correction.

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Appendices

Appendix

Numbers of purchases per user

See the Fig. 6.

Fig. 6
figure6

The figure visualizes the purchase frequency distribution in the raw dataset, considering only users who ever made a purchase during the data collection period. The X-axis represents the minimum number of past purchases and on the Y-axis, the percentage of users in the dataset is shown that are above this threshold. For example, about one third of all users made 5 or more purchases

Feature weights for the occasional users

See the Table 14.

Table 14 Results of the statistical feature weight analysis for the occasional user subset

Examined features

See the Tables 15 and 16.

Table 15 The full list of the examined features (see Sect. 2.3) along with their type and a short explanation
Table 16 List of features not considered by DeepPredict

Additional experimental results

See the Tables 17 and 18.

Table 17 Characteristics of the additional Zalando datasets
Table 18 Hit Rate@10 and MRR@10 results for the additional subsets of random and regular Zalando users

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Jannach, D., Ludewig, M. & Lerche, L. Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts. User Model User-Adap Inter 27, 351–392 (2017). https://doi.org/10.1007/s11257-017-9194-1

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Keywords

  • Recommender systems
  • E-commerce
  • Session-based Recommendation
  • Context-aware Recommendation