CNS: A Task-based Hybrid Collaborative Filtering Recommender Service
Sampath Jayarathna and Frank Shipman
Computer Science & Engineering, Texas A&M University – College Station
ABSTRACT
COMMUNITY NAVIGATION
we propose a task-based hybrid collaborative filtering (CF)
recommender service to support user’s information gathering
tasks. Even with the best Web Search Engines (WSEs), and the
most effective query formulations, information gathering tasks
require people to work through long list of documents to determine
potentially relevant documents.
During information task, useful documents may be long, and cover
multiple subtopics; users may read some segments and ignore
others. In order to record which portion(s) of the document pique
the user’s interests, an explicit interest expressions (e.g.
annotations using WebAnnotate) capturing tool is used .
We propose and implement SVD based LSI to find similarity in
information gathering tasks to find task clusters and use these
clusters for recommendations.
Our method alleviates major challenges in a traditional CF such as
data scalability, synonymy and privacy protection (using interest
profiles via web service). The use of hybrid CF technique in CNS
overcomes CF problems such as sparsity and gray sheep.
The derived User Interest Profile consists of a unique task ID,
documents IDs (URLs) and the respective term vectors (user
annotations).The IPM acts as a local interest profile server where it
collects information from Reading Application (annotation enabled
browser) and this information is aggregated and saved in the user’s
interest profile and broadcasts it to the participating applications.
TASK-BASED RECOMMENDATIONS
Given a new user with an information gathering task consisting of
document IDs and respective term vectors, this can be compared in
the semantic space with the inferred task clusters of the
community.
In order to find the task clusters closely associated with the new
task, the task similarity objective function is used with calculating
the average task similarity per each cluster and finding the Top N
clusters with the highest average task similarity.
CONCLUSION & FUTURE WORK
Our major contributions in this work can be summarized as:
(1) A novel task-based recommender service based on traditional
hybrid CF techniques;
(2) We adapted the LSI in task similarity context which is the
recommendations based on user’s behavior in doing a particular
information gathering task;
(3) We proposed an innovative task clusters in which we
represented the task data in a k-dimensional semantic space in
order to make recommendations based on task similarity
objective function.
In our future works, we are planning to incorporate CNS with the
IPM’s functionality necessary to characterize user interests,
including the ability to collect, parse, and determine similarity
among common forms of Web documents.
REFERENCES
Figure 2. CNS Web Service Architecture
Figure 1. Community Navigation Service Overview
People in their everyday life rely on recommendations from others
in the community for things like reference letters, news, movies,
songs, and travel so forth.
Collaborative Filtering uses the known preferences of a group of
users to make recommendations or predictions of the unknown
preferences for the other users.
The aim of this proposed work is to investigate this similar behavior
in performing everyday information gathering tasks by taking into
consideration similar tasks of community of recommenders.
Each atomic task in the interest profile consists of set of documents
related to the information gathering task and their respective term
vectors. Our goal is to create a N*N task similarity matrix given a
set of N tasks to be clustered.
The SVD based Latent Semantic Indexing (LSI) can take a large
matrix of term document association data and construct a
semantic space where terms and documents that are closely
associated can be detected with Cosine Similarity.
tSim(  ,  ) =
dSim( i, j )



i
j
m n
where, i=1,2…..m, j=1,2…..n
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Acknowledgements : This research is supported by NSF grant 0938074