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THESIS PROPOSAL

Falguni RoyMSSE- 0209

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New similarity computation using trust in user based collaborative recommender

system

Supervised bySheikh Muhammad Sarwar

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Contents

• About Recommender System• Motivation• Research Question• Challenge • Literature Review• Proposed Method• Tentative Timeline

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Recommender System

• Most popular forms of web information customization system

• Used in E-commerce and entertainment based websites

• To predict the 'rating' or 'preference' that user would give to an item

• Approaches–Collaborative filtering–Content-based filtering

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Content-Based Filtering

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Collaborative Filtering(CF)

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Category of CF

• Based on Methodology• Model Based Method

– item recommendation by developing a model– regression, Bayesian network, rule-based and

clustering

• Memory Based Method– a rating matrix– some statistical techniques applied on the rating

matrix.

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• Based on Similarity

Category of CF (Con’t)

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Motivation

Trust

Data Sparsity

Cold Start Users

Cold Start Items

Shilling Attack

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Research Question?

• How to use trust that will be able to perform efficiently and improve systems accuracy???

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Challenge

• How to define trust ?

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Literature Review

• Zhimin Chen et al [1] • mean squared error and evaluation accuracy matrix

• Mohsen Jamali et al. [2] and Qusai Shambour et al [3]

• social trust network

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Proposed Methodology

• Proposed methodology contains three phases:

• Phase 1 : Constructing a neighborhood of similar

users

• Phase 2 : Determining Trust value for all

neighborhood members

• Phase 3 : Similarity computation

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• Constructing a neighborhood of similar users

– Probable neighborhood of similar minded users

– Integration of Pearson Correlation Coefficient (PCC) and Jaccard similarity method

Proposed Methodology (Con’t)

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Proposed Methodology (Con’t)

• Determining Trust value for all neighborhood members– Three components used to evaluate trust – Components are:• MSD: measure the degree of similarity between users• Confidence factor: determine the confidence of target

user on the neighbor’s rating• Profile trust measurement: verifies neighbor’s ratings

items and their effects on the system

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• Similarity computation

– Combine phase 1 & 2 values– Define actual trusted neighbors

Proposed Methodology (Con’t)

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Workflow

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Tentative Timeline

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References

[1]Zhimin Chen, Yi Jiang, Yao Zhao, “A Collaborative Filtering

Recommendation Algorithm Based on User Interest Change and Trust

Evaluation”, JDCTA,volume: 4, number: 9, pages: 106—113,2010

[2] Mohsen Jamali, Martin Ester “TrustWalker: A Random Walk Model for

Combining Trust-based and Item-based Recommendation”, booktitle:

Proceedings of the 15th ACM SIGKDD international conference on

Knowledge discovery and data mining, pages: 397—406, 2009

[3] QusaiShambour, Jie Lu, “A trust-semantic fusion-based recommendation

approach for e-business applications”, Decision Support Systems, volume:

54, number: 1, pages: 768—780, 2012

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