11/09/58 1 computer networks and applications sunantha sodsee information technology king...

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Computer Networks and Applications

Sunantha SodseeInformation Technology

King Mongkut’s University of Technology North Bangkok

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Computer Networks [6]

Types of NetworksEach computer or user in a network is

referred to as a node.The interconnection between the nodes is

referred to as the communication link. In most networks, each node is a personal

computer, but in some cases a peripheral device such as a printer can be a node.

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Computer Networks [6]

The number of links L required between N PCs (nodes) is determined by using the formula

L = N(N−1) / 2

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Network Fundamentals [6]

A network of four PCs.

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A star LAN configuration with a server as the controlling computer.

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A ring LAN configuration.

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A bus LAN configuration.

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Internet Applications [6]

The Internet is a worldwide interconnection of computers by means of a complex network of many networks.

Anyone can connect to the Internet for the purpose of communicating and sharing information with almost any other computer on the Internet.

The Internet is a communication system that accomplishes one of three broad uses: Share resources Share files or data Communication.

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The primary applications of the Internet are:E-mailFile transferThe World Wide WebE-commerceSearchesVoice over Internet ProtocolVideo

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E-mail is the exchange of notes, letters, memos, and other personal communication by way of e-mail software and service companies.

File transfer refers to the ability to transfer files of data or software from one computer to another.

The World Wide Web is a specialized part of the Internet where companies, organizations, the government, or individuals can post information for others to access and use.

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E-commerce refers to doing business over the Internet and other computer networks, usually buying and selling goods and services by way of the Web.

An Internet search allows a person to look for information on any given topic. Several companies offer the use of free search “engines,” which are specialized software that can look for websites related to the desired search topic.

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Voice over Internet Protocol (VoIP) is the technique of replacing standard telephone service with a digital voice version with calls taking place over the Internet.

Video over Internet Protocol. Video or TV over the Internet (IPTV) is becoming more common.

The video (and accompanying audio) is digitized, compressed, and sent via the Internet. It is expected to gradually replace some video transmitted over the air and by cable television systems.

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World Wide Web

A system of globally unique identifiers for resources on the Web and elsewhere, the Universal Document Identifier (UDI), later known as Uniform Resource Locator (URL) and Uniform Resource Identifier (URI);

The publishing language HyperText Markup Language (HTML);

The Hypertext Transfer Protocol (HTTP).

www.wikipedia.org

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World Wide Web

http://www.w3schools.com/html

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E-Commerce

How to enhance E-commerce sales?Browsers into buyersCross-sell

Recommender Systems!!

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What are recommender systems?

Recommender systems are systems which provide recommendations to a user Too much information (information overload) Users have too many choices

Recommend different products for users, suited to their tastes. Assist users in finding information Reduce search and navigation time

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Case Study: Amazon

www.amazon.com

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Personalized Product Recommendation?

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Which Sources of Information?

Sources of information for recommendations: [1]

Browsing and searching data Purchase data Feedback provided by the users Textual comments Expert recommendations E-mail Rating

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Type of Recommendations [2]

Population-basedThe most popular news articles, or searches,

or downloadsFrequently add contentNo user tracking needed.

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Item-to-itemContent-basedOne item is recommended based on the

user’s indication that they like another item. If you like Lord of the Rings, you’ll like Legend.

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Challenges with item-to-item:Getting users to tell you what they like

Financial and time reasons

Getting enough data to make “novel” predictions.

What users really want are recommendations for things they’re not aware of.

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Item-to-itemMost effective when you have metadata that

lets you automatically relate items.Genre, actors, director, etc.

Also best when decoupled from paymentUsers should have an incentive to rate items

truthfully.

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User-based“Users who bought X like Y.”Each user is represented by a vector

indicating his ratings for each product.Users with a small distance between each

other are similar.Find a similar user and recommend things

they like that you haven’t rated.

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User-based Advantages:

Users don’t need to rate much. No info about products needed. Easy to implement

Disadvantages Pushes users “toward the middle” – products with more

ratings carry more weight. How to deal with new products? Many products and few users -> lots of things don’t get

recommended.

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Type of Recommendations: General [1]

Content-based Recommender System Recommend items similar to those users preferred in

the past User profiling is the key Items/content usually denoted by keywords Matching “user preferences” with “item

characteristics” … works for textual information Vector Space Model widely used

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Not all content is well represented by keywords, e.g. images

Items represented by same set of features are indistinguishable

Overspecialization: unrated items not shown Users with thousands of purchases is a problem New user: No history available Shouldn’t show items that are too different, or too

similar

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Collaborative Recommender System• Memory-based collaborative filtering

techniques Main problems: scalability and handling of new

users• Model-based collaborative filtering techniques

High accuracy of prediction No need for searching the whole user-item rating

matrix (grouping users into models)

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Collaborative Recommender System Use other users recommendations (ratings) to judge

item’s utility Key is to find users/user groups whose interests

match with the current user Vector Space model widely used (directions of

vectors are user specified ratings) More users, more ratings: better results Can account for items dissimilar to the ones seen in

the past too Example: Movielens.org

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Different users might use different scales. Possible solution: weighted ratings, i.e. deviations from average rating

Finding similar users/user groups isn’t very easy New user: No preferences available New item: No ratings available Demographic filtering is required Multi-criteria ratings is required

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Type of Recommendations: Example[1]

Cluster ModelsCreate clusters or groupsPut a customer into a categoryClassification simplifies the task of user

matchingMore scalability and performanceLesser accuracy than normal collaborative

filtering method

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Item to item collaboration (one that Amazon.com uses) Compute similarity between item pairs Combine the similar items into recommendation list Vector corresponds to an item, and directions

correspond to customers who have purchased them “Similar items” table built offline Example: Amazon.com

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Knowledge based RS Use knowledge of users and items Conversational Interaction used to establish current

user preferences i.e. “more like this”, “less like that”, “none of those” … No user profiles maintained, preferences drawn

through manual interaction Query by example … tweaking the source example to

fetch results

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How RS Work?

Similarity Measurement [4] For two data objects, X = (x1, x2, . . . , xn) and Y =(y1, y2, . . . ,

yn), the popular Minkowski distance is defined as

where n is the dimension number of the object and xi, yi are the values of the ith dimension of object X and Y respectively, and q is a positive integer. When q = 1, d is Manhattan distance; when q = 2, d is Euclidian distance

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How RS Work?

Similarity wu,v between two users u and v, or wi,j between two items i and j, is measured by computing the Pearson correlation [4]

where the i I summations are over the items that both ∈the users u and v have rated and is the average rating of the co-rated items of the u-th user

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Example

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Prediction and Recommendation Computation

To make a prediction for the active user, a, on a certain item, i, we can take a weighted average of all the ratings on that item according to the following formula [4]

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Example

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Example

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Example

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Challenging: # Users and # Items

Clustering Algorithms

[5]

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Complex Networks

Recommender Systems and Social Web

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Complex Networks

Realistic networks are Complex NetworksBiological Network: How the brain work efficiently? Propagation Network: How viruses propagate

through the computer?Competitor network: How rumors spread out the

human society?Communication Network: How information

transmission exchanges on the Internet ?

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Biotech Industry in USAhttp://ecclectic.ss.uci.edu/~drwhite/

Movie

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Complex Networks What is a complex network?

Observes any form of user behavior

Web surfing logs E-mails transactions Communication over Blogs Friend lists Purchase history on e-

commerce sites Any other kinds action that

demonstrates user intent It creates large scale graph

from all this behavior data

http://www.deqwas.com/en/technology.html

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Recommender Systems and Social We

b [3]

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b [3] Facebook only allows a bidirectional conn

ection among users if user A is connected to B then B is also conn

ected to A Twitter users can follow without being follo

wed user A is linked to B, B is not linked to A.

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b [3]

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b [3]

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b [3] If a user visited certain exhibits and her/his Facebook page mention

s she/he is a "Fan" of certain items, those would be saved for later matching against new visitors profiles.

New visitors would be recommended exhibits that were viewed by people whom they most resemble based on the items they are "Fan".

Find user profiles resembling current visitor's profile, extract tagged photos that are also related to museum's key terms, recommend exhibits relating to those.

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References

[1] Aalap Kohojkar, Yang Liu, Zhan Shi, “Recommender Systems”, March 31, 2008.

[2] Maria Fasli, “Agent Technology for e-Commerce”, http://cswww.essex.ac.uk/staff/mfasli/ATe-Commerce.htm

[3] Amit Tiroshi, Tsvi Kuflik, Judy Kay and Bob Kummerfeld, “Recommender Systems and the Social Web”, International Workshop at UMAP2011 on Augmenting User Models with Real World Experiences to Enhance Personalization and Adaptation, July 15, 2011.

[4] Xiaoyuan Su, Taghi M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques”, Advances in Artificial Intelligence, Vol. 2009, 2009.

[5] Badrul M. Sarwar, George Karypis , Joseph Konstan, and John Riedl, “Recommender Systems for Large-scale E-Commerce: Scalable Neighborhood Formation Using Clustering”, The Fifth International Conference on Computer and Information Technology (ICCIT 2002) , 2002.

[6] Louis E. Frenzel, Jr., “Principles of Electronic Communication Systems”, The third edition, McGraw-Hill, 2008.

11/09/58 1 computer networks and applications sunantha sodsee information technology king...

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