Mining data from Web 2.0 and Location Web Services for Services Recommendation and Products Offer via Mobile media

Hi all,

It has been a while since my last post, but I've returned.  During this period, I was working on master thesis project plan (and finally decided what I will research and work on) as also lecturing a Python training course for a company here at Recife - Brazil. In this post, I will talk more about what I'm planning to do at my master thesis and present some concepts related to Mobile Marketing, Web, Services, Social Media and Recommendation.

Web  2.0  and Location Web Services [ Photo from blog Arrobazona]

Here, I present a resume of my master degree plan.

With the advent of the latest Web 2.0 technologies [1] and social activities ocurring all over the world, more and more people are sharing information and building relationships. They're taking a important role in part of our lives as helping to answer critical questions such as 'what' , 'how', 'where', 'where', 'why' and 'who'.  However, regardless of these questions, one critical issue is how to give all those answers (information) effectively and recommend in a way that may interest people.

One of the possible targets for these activities are the mobile phones. They are a perfect recipient for fetching a variety of data from mobile information like location and ubiquitous content like small text messages, photos, etc. The new generation of multimedia mobile phone, like Iphone, has begun to integrate online web services and location data acquired from location providers such as  Global Positioning System (GPS) and mobile networks.  These new services formed a known and independent research area name as Location Based Services (LBS)[2] [3].  A perfect example of LBS is the Google Maps [4], which aims to help mobile users access to their destinations with real-time traffic information and road conditions. 

Futhermore, the  GPS software vendors, mobile operators and content providers have also gradually to try for the mobile terminal application development. With content created by combining GPS location-based services and latest Web 2.0 technologies (blogs, tagging, comments, social networks, etc.)  it would be possible to provide timely and personalized information and sharing services based on the user's location information. Or even more, use the content provided of the mobile user, to inform the vicinity of restaurants, entertainment and shopping information, etc.

If we look at the existing location-based services,  such as Foursquare [6], Yelp [7] , Gowalla [8] and others, its information is derived from a single content providers (such as map makers or service providers) so there are some relevant limitations [5].  Based on the traditional information retrieving, the location-based-services and companies are giving more emphasis on the dynamics of information and diversity more than the real-time and targeted content services.  Although, the  users want to be able to obtain contextual and identifying content, not just the indexed information based simply on a static database.  

Recently, those LBS services are looking to how to improve their systems by using some game components and   foucusing on the user experience and engagement with augmented-reality functionalities [9]. However, the rise of a large number of Web 2.0 applications (blogs, microblogs, Taggins, forums, Web albums, etc.) indicates that the users have the urgent requirements of direct, fast, useful and personalized information recommendation and sharing services.

So there is a big question here: How to efficiently combine new Web 2.0 applications (Twitter, Facebook, etc.) with location based services and apply to mobile phone ?  Since there are heterogeneous data and services in various formats and different application platforms, how to integrate all this data that can be used as platform-transparency specially for the user? And how to display all this information in a limited display screen of mobile devices, without prejudicing the usability and  the associated costs for the traffic data. Finally,  how to deploy a mobile discovery content  provider by identifying the user preferences and his location in a intelligent way ?

Those questions are doubtless part of a important research topic, and will have a very wide market prospect. Creating mobile advertisements to target a specific audience and a group of users is also one of the challenges in this area and in the Mobile Marketing research field.

Considering the previous statements, my proposal is to study the use of data mining techniques and recommendation engines in order to develop a  recommender system  integrated with Web technologies and location web services in the mobile enviroment. To solve that I will apply a variety of data analysis tools, algorithms to discover valid, novel, potentially useful and understandable patterns and relationships in data.  Design and implement a collaborative recommender algorithm that can analyze the user value-added data obtained from many Web 2.0 applications. Finally,  prototype a location-based data and service middleware based on web services protocols (SOA) to group all this heterogeneous data and services and publish them as one transparent-platform web service. Atacking those fields, I believe at the end of this project, to develop a real case demo and present a complete tool set for mobile data analysis.

That's all, There are many important topics to research and a lot of work to do. My aim is to build a recommender system for events/places/users using data from Twitter/Foursquare and Yelp and other possibility for recommend/offer products in ubiquitous enviroments with prices, items and shopping advertisements. I believe that there's a incredible and promising to research, specially with the  rise of new mobile social web services.

Best regards,

Marcel Caraciolo

References

[1] Tim O'Reilly (2005-09-30). "What Is Web 2.0". O'Reilly Network. 

 http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html.

[2]  Shiode, N., Li, C., Batty, M., Longley, P., & Maguire, D. The impact and penetration of  location-based services. In H. A. Karimi & A.  Hammad (Eds.), Telegeoinformatics:  location-based computing and services, 2004,  pp. 349–366, CRC Press.

[3] Jiang, B., Yao, X. B. Location-based services  and GIS in perspective. Computers, Environment and Urban Systems,Vol.30, No.6, 2006, pp. 712-725.

[4] Google.  Google Maps . At http://maps.google.com

[5] Li, C. User preferences, information transactions and location-based services: A  study of urban pedestrian way finding. Computers, Environment and Urban Systems, Vol.30, No. 6, 2004, pp.726–740.

[6] Foursquare.  Foursquare:. At http://www.foursquare.com

[7] Yelp.  Yelp:. At http://www.yelp.com

[8] Gowalla. Gowalla At http://www.gowalla.com

[9] Maria R. Ebling, Ramón Cáceres, "Gaming and Augmented Reality Come to Location-Based Services," IEEE Pervasive Computing, vol. 9, no. 1, pp. 5-6, Jan.-Mar. 2010.