Hi, everyone. See you again. Time really flies. In the recent lectures, we learned about link analysis and three corresponding link analysis algorithms named respectively HITS(Hyperlink-induced Topic Search), PageRand, EigenRumor. Link analysis refers to analysis of relationships and information flow between individuals, groups, organizations, servers, and other connected entities. Not only do we know about their intrinsic values, but also we should understand clearly their positions in social network relations. Frequenlty, when searching a keywordon web page, we look forward to acquiring the most authoritative content. So it's necessary to use some solutions or approaches to process link analysis.
1. HITS
According to HITS algorithm, after the user enters the keyword, matching algorithm to return the page to calculate two values, one is the hub value, the other is the authoritative value, these two values are interdependent, influencing each other. A good hub represented a page that pointed to many other pages, and a good authority represented a page that was linked by many different hubs.
2. PageRank
PageRank is an algorithm used by Google Search to rank websites in their search engine results. PageRank is a way of measuring the importance of website pages. PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites.
3. EigenRumor
EigenRumor scores each blog entry by weighting the hub and authority scores of the bloggers based on eigenvector calculations. This algorithm enables a higher score to be assigned to the blog entries submitted by a good blogger but not yet linked to by any other blogs based on acceptance of the blogger's prior work.
In social environment, we are faced with the choice of information overload. In order to filter information which is useless for us, we need to use computer to process the huge amount of information. Therefore, recommender systems play an important role in recommending what users should choose. We should know clearly their own interests as well as value of the items or something similar to what users have consumed before. There are two methods to process content-based recommendation. One is similar based recommendation which calculate similarity between user profile and the items to match them. The other is supervised learning which needs negative samples to train a classifer.
In addition, collaborative filtering performs recommend based on the history of user-item interactions by analysing tastes and preferences of users. There are two types of collaborative filtering named neighbourhood-based methods and model-based mathematical models to represent users, items and their relations. Finally, they both generat a ranked list of items for an user.
1. HITS
According to HITS algorithm, after the user enters the keyword, matching algorithm to return the page to calculate two values, one is the hub value, the other is the authoritative value, these two values are interdependent, influencing each other. A good hub represented a page that pointed to many other pages, and a good authority represented a page that was linked by many different hubs.
2. PageRank
PageRank is an algorithm used by Google Search to rank websites in their search engine results. PageRank is a way of measuring the importance of website pages. PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites.
3. EigenRumor
EigenRumor scores each blog entry by weighting the hub and authority scores of the bloggers based on eigenvector calculations. This algorithm enables a higher score to be assigned to the blog entries submitted by a good blogger but not yet linked to by any other blogs based on acceptance of the blogger's prior work.
In social environment, we are faced with the choice of information overload. In order to filter information which is useless for us, we need to use computer to process the huge amount of information. Therefore, recommender systems play an important role in recommending what users should choose. We should know clearly their own interests as well as value of the items or something similar to what users have consumed before. There are two methods to process content-based recommendation. One is similar based recommendation which calculate similarity between user profile and the items to match them. The other is supervised learning which needs negative samples to train a classifer.
In addition, collaborative filtering performs recommend based on the history of user-item interactions by analysing tastes and preferences of users. There are two types of collaborative filtering named neighbourhood-based methods and model-based mathematical models to represent users, items and their relations. Finally, they both generat a ranked list of items for an user.
Reference:
[1] "Facts about Google and Competition". Archived from the original on 4 November 2011. Retrived 12 July 2014.
[2] The EigenRumor algorithm for ranking blogs(2005), by Ko Fujimura, Takafumi Inoue, Masayuki Sugisaki.
[1] "Facts about Google and Competition". Archived from the original on 4 November 2011. Retrived 12 July 2014.
[2] The EigenRumor algorithm for ranking blogs(2005), by Ko Fujimura, Takafumi Inoue, Masayuki Sugisaki.
In order to set up a classifer in supervised learning, some negative samples are required, which seems a little difficult for we usually the items we like.
回覆刪除From your blog, I found some interesting things not including in the class,such as some interesting and inspiring points which not covered in the lecture. form your article, I learnt a lot, thanks for your share.
回覆刪除I like this post a lot because it helps me to review the ranking algorithms we've learnt from the course. The summary is quite precise and shows important features of these algorithms. And I introduce a ranking algorithm intended for blog named B2Rank in my 4th post. It's interesting too. You can have a look and we may have some discussions.
回覆刪除Thanks for review and summarize link analysis in this blog!! The summary is quite precise and shows important features of these algorithms.
回覆刪除The different ranking algorithm are interesting. It would be interesting to go deeper and see example and review of the performance between each algorithm.
回覆刪除Thanks for the summary of the matching algorithm. It would be great if there are more example to elaborate the idea.
回覆刪除Thanks for the summary of the matching algorithm.The summary is quite precise and shows important features of these algorithms.I like this post very much.
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