Context-aware Collaborative Filtering for Learning Interests of Mobile Users George Lee User Context-based Service Control Group Network Laboratories NTT DoCoMo R&D 4/16/2017

Overview The Problem: Mobile users cannot easily get desired information Proposed solution: automatic, personalized, context-aware event notification approach Learns user interests Recommends new information using collaborative filtering Evaluation The Problem: mobile users don’t know where to look to find information they need. Related work: event notification, pub/sub, text retrieval, web portals, search engines, collaborative filtering Research Challenges: First part: an efficient context-aware architecture for classifying and delivering events to mobile users based on detailed subscriptions, An efficient, scalable matching algorithm for this system Second part: Learning context aware user subscriptions automatically, and unobtrusively Key contributions: 1. An efficient context-aware architecture 2. An efficient matching system for complex event models 3. A context-aware collaborative filtering system to learn user preferences My approach: First, the architecture. Key points are, it’s a pub/sub system It automatically classifies events It allows matching both new events and old events It is context aware The topic learner: Learn user preferences Recommends topics using CF, which is essential if there are many topics and contexts My CF approach can take advantage of preference correlations between contexts 4/16/2017

Mobile users can’t easily get relevant information Relevant information is: Appropriate for their context Personalized based on individual interests Current and up-to-date Static menu is inadequate Too many choices Difficult to navigate Not personalized or context-aware Drawbacks of information retrieval Requires queries Not good for new or changing information Rather than thinking about the problem as helping users navigate a service list or menu, think of it as automatically supplying them with information proactively. 4/16/2017

Automatic, personalized, context-aware event notification MIT News CSAIL News The Tech Boston Dining News Italian Restaurants … Central Sq. Sports Red Sox Scores Mobile Handset Context: Going to lab CSAIL News: Talk at 3pm G825 Central Sq. Dining: New café opening Red Sox vs. Yankees: 4-3 (6th inning) 1. Learning user interests 2. Efficiently matching events to users 3. Determining user context Explain the terminology: events There are three main features of this system First point: this is personalized. The system knows that I don’t have a car and prefer taking the train, so it displays train schedules. Similarly, the sports, restaurant, and theme park information are also personalized. Second point: it’s automatic. When an event occurs, the user is automatically notified. If the sports score changes, or if the wait time for the roller coaster changes, the information is automatically updated. Third, it’s context-aware. If the user’s context changes, the information displayed will change. This system uses the context, situation, task ontology, etc. of the SNS, but adds: the capability for personalized information automatic notification. the ability to retrieve information from many sources Automatic Personalized Context-aware 4/16/2017

Matching events and learning user interests Matching Engine decides which users match an event based on event descriptions and user interests User Agent automatically learns user interests for the current context based on user input Event User Event description User interests User input Matching Engine User Agent Event User User Event 4/16/2017

A user agent for learning user interests automatically learns user interests for the current context based on user input Context Event User Event description User interests User input Matching Engine User Agent Event User Challenges: Implicitly learning user interests Recommending info in new contexts User Event 4/16/2017

Learning and automatically updating user interests Automatically recommends new topics based on previous topic ratings Context= (location, task) Implicitly learns user ratings for (context, topic) based on topics users select User Agent Mobile Handset User Interests Selected Topics Topic Recommender Topic Rating Learner Matching Engine Information = events, information organized by topic Users choose topics for more info Implicit rating Context = location, task location and task aware info where this info comes from other elements of context topic rating learner implicit rating. Define “rated” alternative approaches Recommendation many topics, many contexts outputs a list of user interests = topics matching engine delivers events that match these interests Event List Topic 1 Topic 2 Topic 3 Matched events 4/16/2017

Making topic recommendations Recommendations needed for new topics and contexts Possible approaches: Popularity: not personalized Rating History: recommendations based on previous topic ratings Collaborative Filtering (CF): recommendations based on interests of users with similar interests 4/16/2017

Context-aware Collaborative Filtering Is User X interested in “MIT News” for context “Go to lab”? Item = (context, topic) To calculate a recommendation for item (C, T): Find users who have rated (C, T) Find users with similar interests Decide whether to recommend (C, T) based on ratings of similar users User A Yes User B User X User C User D 4/16/2017

Collaborative filtering example (standard approach) Similarity = 0.5 A B C X Y Z User 2 A B C X Y Z User 1 Similarity = 0.5 A B C X Y Z User 3 4/16/2017

Enhanced approach: consider item similarity Item = (context, topic) Give more weight to ratings of similar items Items with same context or same topic are more similar Context similarity Location (TGN) Task Topic similarity (ODP) throw in a picture illustrating similarity between contexts, topics, etc. 4/16/2017

Collaborative filtering example (enhanced approach) Item C: similarity = 1 Item Z: similarity = 0 A B C X Y Z User 2 A B C X Y Z User 1 Item C: similarity = 0 A B C X Y Z Item Z: similarity = 1 User 3 4/16/2017

Recommender Evaluation Evaluate ability of Enhanced CF to provide relevant information in a new context User Interface: app on mobile handset 16 test subjects 8 for data collection 8 for evaluation 50 topics based on i-mode services 2 contexts Going to see a movie in Tokyo Going to Tokyo Disneyland 10 topics per recommender Interleave topics from two recommenders and observe which topics users selected vs. Random vs. Rating History vs. Standard CF Recommender A Recommender B Mobile Handset Event List Topic 1A Topic 1B Topic 2A Topic 2B Topic 3A Topic 3B 4/16/2017

Effective: Enhanced CF can recommend relevant topics in new contexts Compared to other approaches, enhanced CF topics selected 413% more than Random topics 49.7% more than Rating History topics 24.8% more than Regular CF topics More studies needed to increase confidence 4/16/2017

Conclusion I proposed an event notification system for mobile users Automatic Personalized Context-aware Enhanced context-aware collaborative filtering topic recommender Future work Learning and recommendation algorithms Incorporating confidence metrics Context models User studies sparse topic & context spaces how to deal with absolutely new topics & contexts 4/16/2017