Saturday, September 26, 2009

Intelligent Social Networks. Part 2

See the beginning of the article here.

D. Among the numerous discussions in various groups there may be a lot of information that is interesting to me, and I would prefer that the network helped me find it. But without an analysis of my profile, where I described my interests and preferences (i.e. what I'm reading and what I’m liking, what movies I watch and so on), again, it is difficult to achieve anything. The problems with my profile are that: a) it is incomplete, as in it is far from completely representing myself, b) to describe myself, I may not choose the standard semantics, or even a generally accepted terminology and ontology (see problems that led to the paradigm of the Semantic Web), and c) in my description, I am subjective or even try to pass my wishful being as my real self; for example, in my profile I can describe myself as an expert in some area, though I only read a few books on the subject and persuaded my friends, who do not have any experience with this topic, that I am in fact an expert and that they should give me recommendations "confirming" my "expertise". Does this mean that this system should "trust" my profile and recommend me to someone seeking advice or help in this area? One approach to this problem – determining the degree of credibility in my profile (and consequently of my posts), based on the trust analysis inside and outside (e.g. rating of scientific publications) the social network. Another - the auto-generation of my synthesized profile, computed on the basis of my contacts, posts and discussions – much like Google's PageRank, which pulls "semantics" from the use of websites. Such a profile, different from the one I'd write, would be more normalized, "semantically" clear to the network, and comparable with others, and would allow the site to find similarities and differences, classify it, and deduce common interests, yielding more accurate recommendations. Furthermore, such a profile would make online advertising more targeted and effective.

E. The network should be able to analyze (closer to human-style analysis) my correspondence to make my synthesized profile more accurate. To do this, it may use my discussions in various groups, feedback of other users, as well as forward to me, as a person competent in some areas, inquiries or requests for assistance from other users. My responses may then be used to deduce my competency based on feedback, and I may be further tested by having the system forward me requests intended for someone else. "Knowing" so much about me, the network should at this point be able to help me find the right contacts, opportunities and resources, for example, for completing a particular project. Or it may find experts who could answer my questions, give me appropriate links, or even solve my problem, keeping my budget in mind, as well as give me the opportunity to find projects that match my interests and allow me to participate, much like InnoCentive.

But where do we get the computing resources necessary? Today social networks are implemented as centralized systems, using the modern concept of virtualization - Cloud Computing. I think that we should move to a hybrid architecture, employing centralized services, and intelligent software agents which would use the computing power of individual users. This way the agents and resources necessary to shape and polish the synthesized profiles of their owners, as well as to personalize services and make them more intelligent, are proportional with the number of active users. By the way, the social network's developer can generate profit by offering agent templates of varying degrees of intelligence. And the most advanced agents can cooperate with each other to solve various problems, the complexity of which exceeds the capabilities of any individual agent, for example, the search described earlier of a person based on a rough description of him.

But if such an agent is already up and running on one social network, we can agree on a standard like OpenSocial and give it the ability to work cross-boundary, on all social networks (an interoperability problem). Or, at worst, we can create an agent proxy for each network as "personalities", corresponding to each social network environment.

Finally a question arises: is it possible to implement this model in the context of Semantic Social Networks? I am convinced that it is not, and this is why it is necessary to supplement capabilities of the Semantic Web (Web 3.0) with natural language processing, which along with the concept of intelligent software agents is a better suited paradigm for Intelligent Web (Web 4.0); but if we require from these agents greater autonomy, adaptability to the surrounding social environment, as well as cooperation with other agents, then we step into the paradigm of the Adaptive Web (Web 5.0).

P.S. What other ways are there to make money in social networks? I see another way: one can conduct intelligent marketing and analyze how the network formulates an opinion around certain brands, how these opinions can be affected, how they can be predicted, and how one can choose the best marketing strategy and determine to what brands network it is favorable and vice versa (read an article named "Identifying influential spreaders in complex networks").

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