SEARCHING THREADS

• US 20140214833A1
• Filed: 01/31/2013
• Published: 07/31/2014
• Est. Priority Date: 01/31/2013
• Status: Abandoned Application

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Ozonat, Mehmet Kivanc
• Application Number:
  US13/755,771
• Publication Number:
  US 20140214833A1
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification

Abstract

Searching threads can comprise extracting a number of keywords from a number of threads inside a discussion forum in response to a search query, clustering the number of keywords utilizing thread titles and thread content from the within the number of threads, and searching for a thread from within the number of threads that is relevant to the search query based on the clustering.

Claims

1. A method for searching threads, comprising:

extracting a number of keywords from a number of threads inside a discussion forum in response to a search query;

clustering the number of keywords utilizing thread titles and thread content from the within the number of threads; and

searching for a thread from within the number of threads that is relevant to the search query based on the clustering.

2. The method of claim 1, comprising retrieving the relevant thread.

3. The method of claim 1, wherein clustering the number of keywords comprises a hierarchical, multi-view clustering of the number of threads inside the discussion forum.

4. The method of claim 1, wherein extracting the number of keywords comprises:

forming a vector of keywords in a repository of forum threads; and

generating a binary features vector for each thread.

5. The method of claim 4, wherein generating a binary features vector for each thread comprises generating a thread title feature vector and a thread content feature vector for each thread.

6. The method of claim 1, wherein clustering the number of keywords comprises:

growing a thread title data tree and a thread content data tree;

utilizing a Breiman, Freidman, Olshen, and Stone (BFOS) model, pruning the thread title data tree with respect to the thread content data tree;

utilizing the BFOS model, pruning the thread content data tree with respect to the thread title data tree;

in response to a change in a cost function being below a threshold value, terminating pruning of the thread title data tree and the thread content data tree; and

in response to a change in the cost function being above the threshold value, growing a new thread title data tree and a new thread content data tree.

7. The method of claim 1, wherein clustering the number of keywords comprises clustering the number of keywords in an unsupervised setting.

8. A non-transitory computer-readable medium storing a set of instructions executable by a processing resource to:

receive at a consumer product support forum, a search query from a consumer;

extract a number of keywords from a number of threads inside the consumer product support forum;

cluster, utilizing multi-view, hierarchical clustering, the number of extracted keywords into thread title clusters and thread content clusters, such that each keyword is clustered with respect to the other;

search for and retrieve threads relevant to the search query based on the clustering; and

present the retrieved threads in a rank-ordered fashion to the consumer.

9. The non-transitory computer-readable medium of claim 8, wherein the instructions executable to extract the number of keywords comprise instructions executable to extract the number of keywords utilizing term frequency-inverse document frequency, term co-occurrence, and a removal of stop-words.

10. The non-transitory computer-readable medium of claim 8, wherein the thread title clusters and the thread content clusters comprise a limited number of clusters.

11. The non-transitory computer-readable medium of claim 8, wherein the instructions executable to cluster the number of extracted keywords comprise instructions executable to design the thread title cluster and the thread content cluster such that a probability of disagreement between the clusters is minimized, and wherein the thread title cluster and the thread content cluster are designed with respect to one another.

12. A system, comprising:

a processing resource; and

a memory resource communicatively coupled to the processing resource containing instructions executable by the processing resource to;

receive, at a discussion forum associated with a number of threads, a search query;

in response to the search query, build a vector of thread title keywords and a vector of thread content keywords based on the number of threads;

iteratively design a first clustering data tree and a second clustering date tree, wherein the instructions executable to iteratively design comprise instructions executable to;

grow a first clustering data tree utilizing the thread title keyword vector;

grow a second clustering data tree utilizing the thread content keyword vector;

prune the first clustering data tree with respect to the second clustering data tree;

prune the second clustering data tree with respect to the first clustering data tree; and

determine a thread from within the number of threads that is relevant to the search query based on the iteratively designed first and second data trees.

13. The system of claim 12, wherein the instructions executable to prune the first clustering data tree and the second clustering data tree comprise instructions executable to terminate pruning when a change in a cost function is less than a threshold value.

14. The system of claim 12, wherein the instructions executable to grow the first clustering tree and the second clustering tree comprise instructions to grow the first tree as a first tree-structured Gauss mixture vector quantizer (TS-GMVQ) tree and the second tree as a second TS-GMVQ tree.

15. The system of claim 12, wherein the instructions executable to grow the first clustering data tree and the second clustering data tree comprise instructions executable to:

grow the first clustering tree utilizing a first set of subtree functionals; and

grow the second clustering tree utilizing a second set of subtree functionals.