Discovering Causal Dependencies in Mobile Context-Aware Recommenders

1. Discovering Causal Dependencies in Mobile Context-Aware Recommenders Ghim-Eng Yap1, Ah-Hwee Tan School of Computer Engineering Nanyang Technological University Hwee-Hwa Pang School of Information Systems Singapore Management University 1 Ghim-Eng Yap is sponsored by a graduate scholarship from the Agency for Science, Technology and Research (A*STAR).

2. Context-Aware Recommender Systems • What are Recommender Systems? • Systems designed to augment the social recommendation process in aid of decisions. Examples: Information retrieval systems Recommenders for restaurants, books, movies, music, etc • What do we mean by Context? • Any information that can characterize the interaction between an user and the application. Examples: Current weather conditions Availability of vegetarian food

3. Context Acquisition Challenges in Mobile Context-Aware Recommenders • Resource Limitations • We need to identify the minimal set of context for a particular user so as to save on context acquisitions. • Missing Context Values • The missing values may be recoverable given an explicit encoding of the causal dependencies among context.

4. The solution: Bayesian Network • A directed acyclic graph • Encodes the complete causal dependencies among context, and also between the various context and the target variable. • Handles missing context values effectively • This explicit encoding of user-specific causal dependencies among context elements maintains high prediction accuracy.

5. BN Learning Program - CaMML • State-of-the-art automated causal discovery learning method • Searches for Bayesian networks according to causal structure. • Uses the MML (Minimum Message Length) principle as metric. • Employs the Metropolis algorithm for its stochastic sampling. • Basic ideas: • Sample the space of all possible models (subject to constraints); • For each visited real model, compute a representative model and count only on these representative models; • MML posterior of a representative = sum of members’ posteriors; • Best model is the representative with the highest MML posterior. BUT state-of-the-art BN learning schemes are slow when the number of learning variables is large!

6. Adopt a Tiered Context Model • A lower tier deals with a large set of context and item attributes • User context: user preferences as well as physical situation. • Item attributes: properties of each of the items to be ranked. • An upper tier deals with a small set of markers and a item score • Item markers: set of task-specific concerns for a typical user. • Item score: measures suitability of items based on markers.

7. Tiered Model enables Scalable Learning • No need to learn on entire large set of context and attributes • BN learning on just the upper tier to identify minimal set of markers. • These markers and their corresponding context and item attributes are retained for the learning of causal dependency among context. • Final BN is personalized to that user - it encodes dependencies among those context variables important specifically to him/her. • We get a compact and explanatory model of the user-specific context dependencies that can predict on new (future) items.

8. A Restaurant Recommender • A lower tier deals with the large set of context and item attributes • 26 user context: E.g. “preferred category”, “current attire”. • 30 restaurant attributes: E.g. “category”, “attire requirement”. • An upper tier deals with a small set of markers and a item score • 21 markers: E.g. “of desired category”, “attire appropriate”. • 1 score: a value between 0.0 to 1.0 to rank each restaurant.

9. Experimental Validation • Purpose: • To show that the automatic learning of dependencies among context can effectively overcome the two context-acquisition challenges in mobile context-aware recommenders, namely • Identifying the minimal set of context to minimize costs, and • Maintaining accurate prediction with missing context values. • Observation data: • Generated using a set of preference rules that represent different user logics in considering markers, and a set of causal models that state the dependencies among respective subset of context.

10. Datasets Five users are modeled, each with a different preference rule and causal model that are unknown to the learning system. A thousand consistent examples are generated for each user. Score for a restaurant is computed as

11. Learning Minimal Set of Markers • Purpose: • To validate that the minimal set of context truly important to a certain user could be effectively discovered in the Bayesian network that is learned automatically from data. • Procedure: • Extract just the markers and score information from the data; • Learn on this raw data using CaMML, and retain only markers that are directly linked to the score node; • Repeat until a learned model has all its markers linked to score.

12. Observations Performance metric: Discovered Bayesian networks for User 1:

13. Handling Missing Context Values • Purpose: • To verify that automatic learning of BN from data can capture the causal dependencies among context, so that prediction on score remains accurate despite crucial context values being missing. • Procedure: • Using the same datasets as before, we perform a 5x2-fold CV; • Each training set consists of just the score node and the minimal set of restaurant markers, user context and restaurant attributes;

14. Observations We compare prediction accuracy of the learned Bayesian networks (BN) to that of the J4.8 decision trees (DT). • Learning just on markers and score • Both suffer >20% drop in accuracy with missing values. • Learning of user-specific casual dependencies among context • BN clearly outperforms DT.

15. Conclusions • Bayesian network learning from data can be applied to • find minimal set of important context for a user and recommendation task, • exploit causal dependencies among context to overcome missing values. • A two-tiered context model is proposed for scalable learning • abstract large set of context into a smaller set of markers, • fast and scalable learning on only the important variables. • Experiments on a restaurant recommender • reliably discover the minimal set of learning variables, and • accurate prediction even when context values are missing. Any comments to share with us? 