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Adaptation and Evaluation of Recommendationsfor Short-term Shopping Goals
- 1. Adaptation and Evaluation of Recommendations for Short-term Shopping Goals Lukas Lerche, TU Dortmund, Germany Joint work with Dietmar Jannach and Michael Jugovac lukas.lerche@tu-dortmund.de
- 2. E-commerce example ⇨ What is a good recommendation right now? Past purchases: Currently viewed: 1
- 3. User behavior in e-commerce: Different goals: Exploration vs. buying decision Users often focus on certain parts of the product catalog Strong indicator for the type of products they want Their past behavior is also important Might help to improve the recommendations Short-term shopping goals: The target items of a shopping session, based on the customers current needs and sometimes influenced by their general preferences. Short-term shopping goals 2
- 4. Adapted recommendations RS has to adapt to the short-term shopping goals Recommendations should fit the user‘s current interest Algorithm should also personalize the recommendations for the user‘s general (long-term) taste ⇨ Combination of long-term model and short-term contextualization 3 Users x Items Contextu- alization Adapted short-term recommendations Long-term Short-term
- 5. 1. How effective are different strategies to recommend items for short-term shopping goals? Quantify importance of these strategies w.r.t. to long-term models Research questions U x I B Rec X C A Rec Y Rec Z 4 U x I U x I
- 6. 2. How quickly can different recommendation strategies adapt their recommendations? Vary the amount of information that is used to determine the customer’s intentions Research questions U x I A Rec X Rec Y Rec Z A A 5
- 7. Recommendations in context of a specific item Seem to be unpersonalized Context influences the usefulness of the recommendations Context in e-commerce 6 context substitutional reminders complementary
- 8. Used in practice E.g., “Your recently viewed items” at Amazon Remind what the user might have forgotten “Automated wish list” No discovery Reminding the user 7 Recommend to remind Recominder
- 9. Recommendation setting 8 Offline evaluation Common protoval not enough: Matrix completion (“fill the empty cells”) One recommendation list per user No situational information about the user More recent research topics: Context-aware recommendations Time-aware recommender systems Implicit feedback recommendations
- 10. Proposal: Generic protocol, that … 1. works with implicit feedback (user history) 2. is time-aware (navigation/transaction logs) 3. has the ability to simulate current user context ⇨ parameterizable how much context is used (RQ2) Evaluation protocol 9
- 12. 1) Sessionization View View View Sale Cart View View View View Wish Cart View View View View View Cart View Sale Cart Sale View Cart View Sale Session 1 Session 2 Session 11 Session 12 View … TRAINING PHASE Session-aware (timestamp, session id) User goals usually bound to session 10.2
- 13. 2) Split per user by session View View View Sale Cart View View View View Wish Cart View View View View View Cart View Sale Cart Sale View Cart View Sale View Per user training- test split … … TRAINING PHASE Session 1 Session 2 Session 11 Session 12 For each user, use the last sessions as the test set Recommendation task: predict the “Sale”-action Training set Test set 10.3
- 14. 3) Train long-term model View View View Sale Cart View View View View Wish Cart View View View View View Cart View Sale Cart Sale View Cart View Sale View … … TRAINING PHASE Session 1 Session 2 Session 11 Session 12 Users x Items Long-term Learn the long-term model (offline) on the sessions of the training set 10.4
- 15. 4) Control short-term information View View View Sale Cart View View View View Wish Cart View View View View View Cart View Sale Cart Sale View Cart View Sale View … … ONLINE PHASE Session 1 Session 2 Session 11 Session 12 Users x Items Long-term p = 1 previous sessions v = 2 current “Views” Parameters p and v control the amount of most recent user actions that are considered as context Short-term 10.5
- 16. 5) Generate recommendations View View View Sale Cart View View View View Wish Cart View View View View View Cart View Sale Cart Sale View Cart View Sale View … … ONLINE PHASE Session 1 Session 2 Session 11 Session 12 Users x Items Long-term Contextu- alization Adapted short-term recommendations Short-term 10.6
- 17. Not contextualized to recent user actions Established in RS literature Bayesian Personalized Ranking* Factorization Machines* Item-to-Item Collaborative Filtering Baselines: Most popular items, random items Long-term models 11 *Rendle et al., 2009/2010 Users x Items Contextu- alization Adapted short-term recommendations Long-term Short-term
- 18. Take only the most recent actions into account Used, e.g., on Amazon and similar platforms Co-occurring: Users who bought/viewed this, bought … Recently viewed: Browsing history Feature matching: Similar brands, categories, … Short-term adaptation strategies 12 Users x Items Contextu- alization Adapted short-term recommendations Long-term Short-term
- 19. Serialized hybrid approach 13 Different context modeling and combination approaches possible Users x Items Contextu- alization Adapted short-term recommendations Long-term Short-term
- 20. Different context modeling and combination approaches possible We use a serialized hybrid Boost the list position of the short-term recommendations in the long-term recommendation list Serialized hybrid approach Short-term Long-term Adapted 13 Item 5 Item 1 Item 3 Item 1 Item 2 Item 3 Item 4 Item 5 Item 6 Item 7 … Item 1 Item 3 Item 5 Item 2 Item 4 Item 6 Item 7 …
- 21. Feature matching User context (from p previous sessions & v current “View”-actions) Brands: Colors: Price: Category: $$$$ $$$ $ $$ Boost position of items in long-term rec. list: • Puma and Nike products • Mainly inexpensive products • Mainly dark/black products • Sneakers … 14
- 22. Zalando: European online retailer for fashion products Data is extremely sparse Subsets to filter out users & items with low activity Datasets 15 Full Sparse Medium Dense Users 500.000 120.000 38.000 1.900 Items 150.000 40.000 19.000 2.200 “Sale”-actions 1.000.000 680.000 345.000 43.000 “View”-actions 20.000.000 9.800.000 3.900.000 118.000 Min. “Sales” User/Item ─ 3/3 5/5 10/10
- 23. Tmall: Chinese B2C online retailer of the Alibaba Group Comparably small dataset Results similar to Zalando Datasets TMall Users 750 Items 2.000 “Sale”-actions 7.000 “View”-actions 175.000 Min. “Sales” User/Item ─ 16
- 24. Recall N = 10, k = 100 Precision: 1 1+𝑘 ∙ 𝑟𝑒𝑐𝑎𝑙𝑙 MRR (mean reciprocal rank) Metrics + ranking Item 8 Item 4 Item 1 Item 3 … Item k Item T Item T Item 1 Item 2 Item 3 Item 4 …Item k Target item k irrelevant items Top N 17
- 25. Results: Short-term strategies 18 v = Revealed # of “View” actions and p = 2 previous sessions as context Dataset: Zalando dense
- 26. Contextualization ⇨ higher accuracy Even when little is known about the current goals Feature matching Brand and categories work well in this domain Best results with a combination of feature matching and recently viewed Results: Short-term strategies 19
- 27. Recently viewed performs remarkably well here Reminding the user also seems to work in practice At least as a separate recommendation list, see Amazon Challenge in practice: Reminding vs. discovery Recall is unclear to capture usefulness Obviousness of recommendations can be problematic “The value of reminders” Results: Reminding the user 20
- 28. The long-term model becomes less relevant when more context is available to the algorithm Short-term model works well even without the long-term model (random baseline) Results: Long-term models 21 Revealed # of p previous sessions and v current “View” actions as context + (FM + RV)
- 29. How to treat different types of implicit feedback? What is the benefit of multiple recommendation lists? Does offline accuracy overestimate the usefulness of the recommendations? Discussion 22
- 30. Explored the role of long-term models and short-term interests In this domain short-term dominates long-term Proposed a new evaluation protocol Simulation of short-term goals Even simple contextualization works well Recominders Summary 23
- 31. Adaptation and Evaluation of Recommendations for Short-term Shopping Goals Lukas Lerche, TU Dortmund, Germany Joint work with Dietmar Jannach and Michael Jugovac lukas.lerche@tu-dortmund.de Questions?