Archive for the ‘Context-aware’ Category

Context-Aware Recommender Systems 2012 [Identity and Context?]

Tuesday, September 11th, 2012

Context-Aware Recommender Systems 2012 (In conjunction with the 6th ACM Conference on Recommender Systems (RecSys 2012))

I usually think of recommender systems as attempts to deliver content based on clues about my interests or context. If I dial 911, the location of the nearest pizza vendor probably isn’t high on my lists of interests, etc.

As I looked over these proceedings, it occurred to me that subject identity, for merging purposes, isn’t limited to the context of the subject in question.

That is some merging tests could depend upon my context as a user.

Take my 911 call for instance. For many purposes, a police substation, fire station, 24 hour medical clinic and a hospital are different subjects.

In a medical emergency situation, for which a 911 call might be a clue, all of those could be treated as a single subject – places for immediate medical attention.

What other subjects do you think might merge (or not) depending upon your context?

Table of Contents

  1. Optimal Feature Selection for Context-Aware Recommendation Using Differential Relaxation
    Yong Zheng, Robin Burke, Bamshad Mobasher.
  2. Relevant Context in a Movie Recommender System: Users’ Opinion vs. Statistical Detection
    Ante Odic, Marko Tkalcic, Jurij Franc Tasic, Andrej Kosir.
  3. Improving Novelty in Streaming Recommendation Using a Context Model
    Doina Alexandra Dumitrescu, Simone Santini.
  4. Towards a Context-Aware Photo Recommender System
    Fabricio Lemos, Rafael Carmo, Windson Viana, Rossana Andrade.
  5. Context and Intention-Awareness in POIs Recommender Systems
    Hernani Costa, Barbara Furtado, Durval Pires, Luis Macedo, F. Amilcar Cardoso.
  6. Evaluation and User Acceptance Issues of a Bayesian-Classifier-Based TV Recommendation System
    Benedikt Engelbert, Karsten Morisse, Kai-Christoph Hamborg.
  7. From Online Browsing to Offline Purchases: Analyzing Contextual Information in the Retail Business
    Simon Chan, Licia Capra.

Posted in Context, Context-aware, Identity, Recommendation | No Comments »

Sarcastic Computers?

Thursday, May 31st, 2012

You may have seen the headline: Could Sarcastic Computers Be in Our Future? New Math Model Can Help Computers Understand Inference.

And the lead for the article sounds promising:

In a new paper, the researchers describe a mathematical model they created that helps predict pragmatic reasoning and may eventually lead to the manufacture of machines that can better understand inference, context and social rules.

Language is so much more than a string of words. To understand what someone means, you need context.

Consider the phrase, “Man on first.” It doesn’t make much sense unless you’re at a baseball game. Or imagine a sign outside a children’s boutique that reads, “Baby sale — One week only!” You easily infer from the situation that the store isn’t selling babies but advertising bargains on gear for them.

Present these widely quoted scenarios to a computer, however, and there would likely be a communication breakdown. Computers aren’t very good at pragmatics — how language is used in social situations.

But a pair of Stanford psychologists has taken the first steps toward changing that.

Context being one of those things you can use semantic mapping techniques to capture, I was interested.

Jack Park pointed me to a public PDF of the article: Predicting pragmatic reasoning in language games

Be sure to read the entire file.

A blue square, a blue circle, a green square.

Not exactly a general model for context and inference.

Posted in Context, Context Models, Context-aware, Inference | No Comments »

Managing context data for diverse operating spaces

Wednesday, May 16th, 2012

Managing context data for diverse operating spaces by Wenwei Xuea, Hung Keng Pungb, and Shubhabrata Senb.

Abstract:

Context-aware computing is an exciting paradigm in which applications perceive and react to changing environments in an unattended manner. To enable behavioral adaptation, a context-aware application must dynamically acquire context data from different operating spaces in the real world, such as homes, shops and persons. Motivated by the sheer number and diversity of operating spaces, we propose a scalable context data management system in this paper to facilitate data acquisition from these spaces. In our system, we design a gateway framework for all operating spaces and develop matching algorithms to integrate the local context schemas of operating spaces into a global set of domain schemas upon which SQL-based context queries can be issued from applications. The system organizes the operating space gateways as peers in semantic overlay networks and employs distributed query processing techniques over these overlays. Evaluation results on a prototype implementation demonstrate the effectiveness of our system design.

This article came up in a sweep for “semantic overlay networks.”

Encouraging recognition that results may need to vary based on physical context. Who knows? Perhaps recognition that the terminology for one domain and its journals/authors/monographs has different semantics than other domains.

Imagine that, a system that manages queries across semantic domains for users, as opposed to users having to understand all the possible semantic domains in advance to have useful query results (or better query results).

Perhaps the “context” metaphor may be a useful one in marketing topic maps. Less aggressive than “silo.” Let the client come up with that to characterize competing agencies or information sources.

“Context” in the sense of physical space is popular among the smart phone crowd so don’t neglect that as an avenue for topic maps as well. (Looking at your surroundings would mean breaking eye contact with your phone. Might miss an ad or something.)

Posted in Context-aware, Semantic Overlay Network | No Comments »

Context-aware intelligent recommender system

Tuesday, October 5th, 2010

Context-aware intelligent recommender system Authors: Mehdi Elahi Keywords: active learning, classification, context-aware, fuzzy logic, recommendation systems, recommenders

Abstract:

This demo paper presents a context-aware recommendation system. The system mines data from user’s web searches and other sources to improve the presentation of content on visited web pages. While user is browsing the internet, a memory resident agent records and analyzes the content of the webpages that were either searched for or visited in order to identify topic preferences. Then, based on such information, the content of requested web page is ranked and classified with different styles. The demo shows how a music weblog can be modified automatically based on user’s affinities.

Context-aware recommendation systems help present relevant information in large topic maps but I am more interested in their use for authoring systems.

Automatic construction of topics/roles/associations based on prior choices (for user approval) comes to mind.

Not a tool for a casual author but certainly a power tool for professional information explorers. (librarians?)

Posted in Classification, Context-aware, Fuzzy Logic | No Comments »