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CUUS2079-09 CUUS2079-Zafarani 978 1 107 01885 3 January 13, 2014 17:28

9

Recommendation in Social Media

Individuals in social media make a variety of decisions on a daily basis.

These decisions are about buying a product, purchasing a service, adding a

friend, and renting a movie, among others. The individual often faces many

options to choose from. These diverse options, the pursuit of optimality, and

the limited knowledge that each individual has create a desire for external

help. At times, we resort to search engines for recommendations; however,

the results in search engines are rarely tailored to our particular tastes and

are query-dependent, independent of the individuals who search for them.

To ease this process, applications and algorithms are developed to help

individuals decide easily, rapidly, and more accurately. These algorithms are

tailored to individuals’ tastes such that customized recommendations are

available for them. These algorithms are calledrecommendation algorithms

orrecommender systems.

RECOMMENDER

SYSTEM

Recommender systems are commonly used for product recommenda-

tion. Their goal is to recommend products that would be interesting to

individuals. Formally, a recommendation algorithm takes a set of usersU

and a set of itemsIand learns a functionfsuch that

f:U×I→R (9.1)

In other words, the algorithm learns a function that assigns a real value to

each user-item pair (u,i), where this value indicates how interested useru

is in itemi. This value denotes theratinggiven by useruto itemi. The rec-

ommendation algorithm is not limited to item recommendation and can be

generalized to recommending people and material, such as, ads or content.

Recommendation vs. Search

When individuals seek recommendations, they often use web search

engines. However, search engines are rarely tailored to individuals’ needs

and often retrieve the same results as long as the search query stays the

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