Domain — specific search focuses on one area of knowledge. Applying broad based ranking algorithms to vertical search domains is not desirable. The broad based ranking model builds upon the data from multiple domains existing on the web. Vertical search engines attempt to use a focused crawler that index only relevant web pages to a predefined topic. With Ranking Adaptation Model, one can adapt an existing ranking model of a unique new domain. The binary classifiers classify the members of a given set of objects into two groups on the basis of whether they have some property or not. If it is property of relevancy, it is returned to the search query of that particular domain vertical. Sponsored ads are then placed alongside the organic search results and they are ranked with the help of bid, budget and quality score. The ad with the highest bid is placed first in the ad listings. Later, the ad with a maximum quality score is found by click through logs which is replaced in first position. Thus, both organic search and sponsored ads are returned for the specific domain, making it easy for the users to get access to real time ads and connect directly with advertisers as well as to get information on the search query.

Ranking model adaptation for domain specific mining using binary classifier for sponsored ads

Utility-based data mining is a rapidly developing research area in all types of utility factors in data mining processes. High utility mining is an emerging domain in Utility based data mining which is aimed at finding only itemsets that possess high utility value. Many algorithms are aiming at finding high utility itemsets. A well-known algorithm called Improved Fast Utility Mining (iFUM) lacks in functional accuracy when applied to large volumes of database. Hence, an Efficient Utility Mining (EUM) algorithm proposed in this paper to find all utility itemsets within the given utility threshold and timestamp. Moreover, a novel method is proposed in this paper for generating different types of utility frequent itemsets such as High Utility High Frequency (HUHF), High Utility Low Frequency (HULF), Low Utility High Frequency (LUHF), Low Utility Low Frequency (LULF) using a combination of Efficient Utility Mining (EUM) algorithm and Frequent Itemset Mining (FIM) algorithm for a given time interval.

S. Kousalya

Papers

Frequent Itemsets Generation using Efficient Utility Mining Algorithm