Skip to main content

Aardvark paper in WWW 2010: about social search

Aardvark, which powers vark.com, published an interesting paper entitled "Anatomy of a Large-Scale Social Search Engine" at WWW 2010.

The paper talks about the social search engine applied in vark.com. The search engine is based on social graphs and topics instead of keyword. The paper addresses search engine like Google as library paradigm and Aardvark as village paradigm. The search engine of village paradigm gets answers by asking the one who are expert in the underlying topic in social graphs. In library paradigm, the search engine needs to figure out what a user what based on keywords, search history and user profile, which considers to be a very difficult task. The village paradigm leaves the difficult part to human being, so, only problem is to find the right person.

The model of Aardvark considers that a user u1 asks a question q, and the search engine should find the right user u2 to provide the answer. Aardvark associates both users and questions to topics. Aardvark extracts and stores a set of topics for every users. When a question q of user u1 comes, Aardvark extracts topics t from the question, and find the best user u2 in u1's social graph according to topics t. Aardvark need not to index all the questions/answers/articles on the web, but only topics and social graphs of users. The topics may be considered as the relationship between text and people, and the social graphs are the relationship between people.

Comments

Popular posts from this blog

A simple implementation of DTW(Dynamic Time Warping) in C#/python

DTW(Dynamic Time Warping) is a very useful tools for time series analysis. This is a very simple (but not very efficient) c# implementation of DTW, the source code is available at  https://gist.github.com/1966342  . Use the program as below: double[] x = {9,3,1,5,1,2,0,1,0,2,2,8,1,7,0,6,4,4,5}; double[] y = {1,0,5,5,0,1,0,1,0,3,3,2,8,1,0,6,4,4,5}; SimpleDTW dtw = new SimpleDTW(x,y); dtw.calculateDTW(); The python implementation is available at  https://gist.github.com/3265694  . from python-dtw import Dtw import math dtw = Dtw([1, 2, 3, 4, 6], [1, 2, 3, 5],           distance_func=lambda x, y: math.fabs(x - y)) print dtw.calculate() #calculate the distance print dtw.get_path() #calculate the mapping path

Change the default user when start a docker container

When run(start) a docker container from an image, we can specify the default user by passing -u option in command line(In https://docs.docker.com/engine/reference/run/#user ). For example docker run -i -t -u ubuntu ubuntu:latest /bin/bash We can also use the USER instruction in DOCKERFILE to do the same thing(In https://docs.docker.com/engine/reference/builder/#user), note that the option in command line will override the one in the DOCKERFILE. And there is actually another way to start a container with neither DOCKERFILE nor -u option, just by a command like: docker run -i -t ubuntu:latest /bin/bash # with ubuntu as the default user This happens when your start the container from an image committed by a container with ubuntu as the default user. Or in detail: Run a container from some basic images, create ubuntu user inside it, commit the container to CUSTOM_IMAGE:1 . Run a container from CUSTOM_IMAGE:1 with "-u ubuntu" option, and commit the container to CUSTOM...

Notes on Sequential Pattern Mining (2) -- Partial Order Pattern Mining and Contrast Mining

1. In , the authors induce TEIRESIAS algorithms to mining combinatorial patterns with gap constraints in biological sequences. The patterns TEIRESIAS mined is similiar with the common sequential patterns, but it could contain "." the wild card which is also in the alphbel of the sequences database standing for any other item available, for example pattern "A..B" is a length-4 pattern, with two arbitrary items between the first A and the last B. Patterns "AC.B", "AADB" are all said to be more specific than pattern "A..B". TEIRESIAS mining all the maximal patterns () with a support over a min threshold K. There some key points of TEIRESIAS algorithms: 1)The growth of the patterns The growth of the patterns is accomplished by convolute current pattern by a short length pattern. Pattern A and pattern B are convolutable if the last L(very small) characters of pattern A is the same as the first L characters of pattern B, then ...