Research talk 4 - Context-Awareness In Information Retrieval and Recommender Systems
This is a tutorial from Yong Zheng at WI2016.
Currently, the use of most information systems are independent from their users’ environmental factors, say, systems disregard the context of users. However these factors can work as critical information supplement, especially when the system are unable to make users’ needs explicit based merely on queries and profile information. Obviously, information retrieval and recommender system are the two most appropriate applications to utilize contextual information. The main difference between these two is that query is explicitly required by information retrieval, but not by recommender system.
So what’s context? A well-recognized definition is Context is any information that can be used to characterize the situation of an entity. The contexts can be collected from multiple sources, like sensors, user inputs(survey or user interactions) and inference from user reviews. The most common contextual variables include:
- Time and Location
- User intent or purpose
- User emotional states
- Topics of interests, e.g., apple vs. Apple
- Others: companion, weather, budget, etc
Five contextual modeling methods are highlighted here:
- Tensor Factorization, 2010
- Context-aware Matrix Factorization, 2011
- Factorization Machines, 2011
- Deviation-Based Contextual Modeling, 2014
- Similarity-Based Contextual Modeling, 2015
And a few worth-noting challenges:
- Numeric v.s. Categorical Context Information: various kinds of context features.
- Explanations by Context: hard to explain the recommended results.
- New User Interfaces and Interactions: design new UI to help collect/interact/explain context information.
- User Intent Predictions or References in IR and RecSys
- Cold-start and Data Sparisty Problems