ms research progress presentation

Progress of Progress of Research WorkResearch Work

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User Similarity Computation for User Similarity Computation for Collaborative Filtering Using Collaborative Filtering Using

Dynamic Implicit TrustDynamic Implicit Trust

04/15/23 2

Presented byPresented byFalguni RoyMSSE- 0209

Supervised bySupervised bySheikh Muhammad Sarwar

04/15/23 3User Similarity Computation for CF

Using Dynamic Implicit Trust

ContentsContents

About Recommender System Motivation Literature Review Proposed Methodology Dataset Publication Remarkable Reviews

04/15/23 4User Similarity Computation for CF Using Dynamic Implicit Trust

About About Recommender Recommender SystemSystem


• Most popular forms of web information customization system

• Used in E-commerce and entertainment based websites

– amazon.com

– Netflix.com

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


Category of RSCategory of RS

• Based on the information filtering approach, there

are two category of recommender system [1]

Content based filtering, and

Collaborative based filtering


Content Based FilteringContent Based Filtering


Collaborative Filtering(CF) Collaborative Filtering(CF)

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• Most popular approach [2,3]

User Similarity Computation for CF Using Dynamic Implicit Trust

Category of CFCategory 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.


• Based on Similarity

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Category of CF (Cont’)Category of CF (Cont’)


Trust based Recommender SystemTrust based Recommender System

• Guo et al. defines trust in recommender system as

“Trust is defined as one's belief towards the ability

of others in providing valuable ratings“ [4]

• Express the integrity in the relationship between

two entities.

• Trust used to scale similarity


Trust Properties Trust Properties

• According to trust theory [5]

Asymmetry

Transitivity

Dynamicity

Context Dependence


Type of TrustType of Trust

• On the basis of trust computation

Explicit Trust

Implicit Trust


Explicit Trust Explicit Trust

• Trust value is calculated by pre-existing

social link between users

• The link is defined as

By defining web of trust

Assign a trust statement


Implicit TrustImplicit Trust

• Derived from user-item rating matrix by

analyzing rating pattern, rating value and

historical behavior of ratings of the users

• Trustworthiness of a user is determined by the

prediction accuracy of a user in the past [6]


MotivationMotivation

Problem of existing Trust based RSProblem of existing Trust based RS Don’t concern about users’ changing interestsDon’t concern about users’ changing interests Treats users’ similarity as symmetricTreats users’ similarity as symmetric


Literature ReviewLiterature Review



• Qusai Shambour et al. [7] (TM1)Computed trust based on mean squared distance (MSD)

and propagated trust based, on the MoleTrust matric

• Lathia et al. [9] (TM2)Proposed a trust method to define degree of trust by

tracking the value of ratings provided by other usersTrust is defined as the average of provided values over all

the rated items

• Papagelis et al. [10] (TM3)Define trust through user similarity computed by Pearson

correlation coefficient


• Hwang et al. [8] (TM4)

Computed trust by averaging the prediction error on co-

rated items

• O'Donovan et al. [6] (TM5)

A rating provided by users is correct if the absolute

difference between the predicted rating and the actual

rating is smaller than a threshold

Profile level trust and item level trust


A comparison of different trust metrics in A comparison of different trust metrics in terms of trust properties [12][13]terms of trust properties [12][13]

Methods Asymmetry Transitivity Dynamicity Context Dependence

TM1 [7] No Yes No No


TM3 [10] No Yes, iff s> ϴϴ = 0.707

No No




Proposed Proposed MethodologyMethodology



Trust Based Similarity ComputationTrust Based Similarity Computation

• The proposed system consists of the following modules: Trust Computation module (TC), Similarity Computation module (SC) andCombined Trust and Similarity Computation module (CTSC)


Similarity Computation ModuleSimilarity Computation Module

• Extract a neighborhood of similar minded users for the target

user

• Similarity is calculated by integrating Pearson Correlation

Coefficient (PCC) and Jaccard similarity method [8] defined

as JPCC


Trust Computation ModuleTrust Computation Module

• Implicit trust is populated by defining the similarity or degree

of similarity between the users [12]

• Proposed a new method for determining the implicit trust

between the users as an integration of Mean Square Difference

(MSD) and Confidence and consider users’ changing interests

to support trust properties.


Dataset Dataset


PublicationPublication



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THE 4TH INTERNATIONAL CONFERENCE ON ANALYSIS OF THE 4TH INTERNATIONAL CONFERENCE ON ANALYSIS OF IMAGES, SOCIAL NETWORKS, AND TEXTSIMAGES, SOCIAL NETWORKS, AND TEXTS

(Yekaterinburg, Russia)(Yekaterinburg, Russia)

AIST 2015: 24% Acceptance rate24% Acceptance rate

Will be published in the Springer’s

Communications in Computer and

Information Science series

The conference will be held on 9th

through Saturday, 11th of April 2015

at Russia


Remarkable ReviewsRemarkable Reviews



ReviewsReviews

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The paper addresses an important problemaddresses an important problem in recommender systems, that is, how to incorporate trust into the process. The authors give a comprehensive overview of the related workcomprehensive overview of the related work. Then, two components of the TJPCC model are introduced one by one. The proposed final TJPCC has the key properties typical for trust. Four existing baselines are used for comparison. According to the experimental results, the proposed TJPCC method achieves better results.

Pros – The method is simple to implement The method is simple to implement

– The problem is very relevant. The problem is very relevant.

– Good overview of the literatureGood overview of the literature,

Cons– Use more data sets for evaluationUse more data sets for evaluation


ReviewsReviews

The paper starts with a detailed introduction, followed by a broad section on related work for trust based and dynamic recommender systems. Then, the method is presented as a combination of similarity and trust scores. Overall, the paper is well written and interesting the paper is well written and interesting to read. The descriptions are elaborateto read. The descriptions are elaborate. I recommend this paper for publication


ReviewsReviews

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The authors present a new similarity between the users using a novel dynamic trust definition among users. The new trust definition satisfies desirable properties such as being asymmetric, transitive and dynamic, and experimental results on movie database IMDB are favorable. However, more experimental results more experimental results that will further that will further strengthen the proposed method would be strengthen the proposed method would be preferablepreferable.


Findings for Next StepFindings for Next Step

Usage of more data sets for evaluation

More exploration of the properties of the

proposed algorithm

Experimenting the proposed method with

different evolution metrics


References References 1. R. Burke, “Hybrid web recommender systems," in The adaptive web, pp. 377-408, Springer, 2007

2. M. J. Pazzani, “A framework for collaborative, content-based and demographic fitering," Articial Intelligence Review, vol. 13, no. 5-6, pp. 393-408, 1999.

3. F. Cacheda, V. Carneiro, D. Fernandez, and V. Formoso, “Comparison of collaborative fitering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems," ACM Transactions on the Web (TWEB), vol. 5, no. 1, p. 2, 2011.

5. Guibing Guo. “Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems”. In Proceedings of the 7th ACM conference on Recommender systems, pages 451-454. ACM, 2013

6. Soude Fazeli, Babak Loni, Alejandro Bellogin, Hendrik Drachsler, and Peter Sloep. “Implicit vs. explicit trust in social matrix factorization”. In Proceedings of the 8th ACM Conference on Recommender systems, pages 317-320. ACM, 2014.

7. John O'Donovan and Barry Smyth. Trust in recommender systems. In Proceedings of the 10th international conference on Intelligent user interfaces, pages 167-174. ACM, 2005.

8. Q. Shambour and J. Lu, “A trust-semantic fusion-based recommendation approach for e-business applications,” Decision Support Systems, vol. 54, no. 1, pp. 768–780, 2012.

9. Chein-Shung Hwang and Yu-Pin Chen. “Using trust in collaborative ltering recommendation”. In New trends in applied articial intelligence, pages 1052-1060. Springer, 2007.

10. N. Lathia, S. Hailes, and L. Capra. “Trust-based collaborative filtering”. In Trust Management II, 2008.

11. M. Papagelis, D. Plexousakis, and T. Kutsuras. Alleviating the sparsity problem of collaborative filtering using trust inferences. In Trust management. 2005.

12. Guibing Guo, Jie Zhang, Daniel Thalmann, Anirban Basu, and Neil Yorke-Smith. “From ratings to trust: an empirical study of implicit trust in recommender systems.” 2014

13. Soude Fazeli, Babak Loni, Alejandro Bellogin, Hendrik Drachsler, and Peter Sloep. “Implicit vs. explicit trust in social matrix factorization”. In Proceedings of the 8th ACM Conference on Recommender systems, pages 317-320. ACM, 2014.


ms research progress presentation

Education

implicit trustimplicit

web of trust

trust theory

trust statement

user i

useritem rating matrix

collaborative based

category of recommender