Using a Trust Network To Improve Top-N Recommendation

Slides:



Advertisements
Similar presentations
Mohsen Jamali, Martin Ester Simon Fraser University Vancouver, Canada UBC Data Mining Lab October 2010.
Advertisements

Collaborative Filtering Sue Yeon Syn September 21, 2005.
COMP423 Intelligent Agents. Recommender systems Two approaches – Collaborative Filtering Based on feedback from other users who have rated a similar set.
1 RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation Xi Chen, Xudong Liu, Zicheng Huang, and Hailong.
COLLABORATIVE FILTERING Mustafa Cavdar Neslihan Bulut.
Active Learning and Collaborative Filtering
Learning to Recommend Hao Ma Supervisors: Prof. Irwin King and Prof. Michael R. Lyu Dept. of Computer Science & Engineering The Chinese University of Hong.
Item-based Collaborative Filtering Idea: a user is likely to have the same opinion for similar items [if I like Canon cameras, I might also like Canon.
Mohsen Jamali, Martin Ester Simon Fraser University Vancouver, Canada ACM RecSys 2010.
Rubi’s Motivation for CF  Find a PhD problem  Find “real life” PhD problem  Find an interesting PhD problem  Make Money!
CMPT 884, SFU, Martin Ester, Recommender Systems Martin Ester Simon Fraser University School of Computing Science CMPT 884 Spring 2009.
A shot at Netflix Challenge Hybrid Recommendation System Priyank Chodisetti.
TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation Mohsen Jamali & Martin Ester Simon Fraser University, Vancouver,
1 Collaborative Filtering and Pagerank in a Network Qiang Yang HKUST Thanks: Sonny Chee.
Top-N Recommendation Algorithm Based on Item-Graph
Recommender systems Ram Akella February 23, 2011 Lecture 6b, i290 & 280I University of California at Berkeley Silicon Valley Center/SC.
Analysis of Recommendation Algorithms for E-Commerce Badrul M. Sarwar, George Karypis*, Joseph A. Konstan, and John T. Riedl GroupLens Research/*Army HPCRC.
Social Context Based Recommendation Systems and Trust Inference Student: Andrea Manrique ID: ITEC810, Macquarie University1 Advisor: A/Prof. Yan.
Combining Content-based and Collaborative Filtering Department of Computer Science and Engineering, Slovak University of Technology
Item-based Collaborative Filtering Recommendation Algorithms
Performance of Recommender Algorithms on Top-N Recommendation Tasks
Temporal Event Map Construction For Event Search Qing Li Department of Computer Science City University of Hong Kong.
Mao Ye, Peifeng Yin, Wang-Chien Lee, Dik-Lun Lee Pennsylvania State Univ. and HKUST SIGIR 11.
Performance of Recommender Algorithms on Top-N Recommendation Tasks RecSys 2010 Intelligent Database Systems Lab. School of Computer Science & Engineering.
Citation Recommendation 1 Web Technology Laboratory Ferdowsi University of Mashhad.
Distributed Networks & Systems Lab. Introduction Collaborative filtering Characteristics and challenges Memory-based CF Model-based CF Hybrid CF Recent.
Item Based Collaborative Filtering Recommendation Algorithms Badrul Sarwar, George Karpis, Joseph KonStan, John Riedl (UMN) p.s.: slides adapted from:
Collaborative Filtering Recommendation Reporter : Ximeng Liu Supervisor: Rongxing Lu School of EEE, NTU
LOGO Recommendation Algorithms Lecturer: Dr. Bo Yuan
Agenda  Summary and outlook –Summary –Outlook –References.
1 Formal Models for Expert Finding on DBLP Bibliography Data Presented by: Hongbo Deng Co-worked with: Irwin King and Michael R. Lyu Department of Computer.
A Hybrid Recommender System: User Profiling from Keywords and Ratings Ana Stanescu, Swapnil Nagar, Doina Caragea 2013 IEEE/WIC/ACM International Conferences.
1 Applying Collaborative Filtering Techniques to Movie Search for Better Ranking and Browsing Seung-Taek Park and David M. Pennock (ACM SIGKDD 2007)
Presented by: Apeksha Khabia Guided by: Dr. M. B. Chandak
Classical Music for Rock Fans?: Novel Recommendations for Expanding User Interests Makoto Nakatsuji, Yasuhiro Fujiwara, Akimichi Tanaka, Toshio Uchiyama,
1 Social Networks and Collaborative Filtering Qiang Yang HKUST Thanks: Sonny Chee.
EigenRank: A Ranking-Oriented Approach to Collaborative Filtering IDS Lab. Seminar Spring 2009 강 민 석강 민 석 May 21 st, 2009 Nathan.
Badrul M. Sarwar, George Karypis, Joseph A. Konstan, and John T. Riedl
The Effect of Dimensionality Reduction in Recommendation Systems
Collaborative Data Analysis and Multi-Agent Systems Robert W. Thomas CSCE APR 2013.
Temporal Diversity in Recommender Systems Neal Lathia, Stephen Hailes, Licia Capra, and Xavier Amatriain SIGIR 2010 April 6, 2011 Hyunwoo Kim.
A more efficient Collaborative Filtering method Tam Ming Wai Dr. Nikos Mamoulis.
Measuring Association Rules Shan “Maggie” Duanmu Project for CSCI 765 Dec 9 th 2002.
Evaluation of Recommender Systems Joonseok Lee Georgia Institute of Technology 2011/04/12 1.
Improving Recommendation Lists Through Topic Diversification CaiNicolas Ziegler, Sean M. McNee,Joseph A. Konstan, Georg Lausen WWW '05 報告人 : 謝順宏 1.
Recommender Systems Debapriyo Majumdar Information Retrieval – Spring 2015 Indian Statistical Institute Kolkata Credits to Bing Liu (UIC) and Angshul Majumdar.
Intelligent DataBase System Lab, NCKU, Taiwan Josh Jia-Ching Ying, Eric Hsueh-Chan Lu, Wen-Ning Kuo and Vincent S. Tseng Institute of Computer Science.
Recommender Systems with Social Regularization Hao Ma, Dengyong Zhou, Chao Liu Microsoft Research Michael R. Lyu The Chinese University of Hong Kong Irwin.
GeoMF: Joint Geographical Modeling and Matrix Factorization for Point-of-Interest Recommendation Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, EnhongChen,
Community-Based Link Prediction/Recommendation in the Bipartite Network of BoardGameGeek.com Brett Boge CS 765 University of Nevada, Reno.
Page 1 A Random Walk Method for Alleviating the Sparsity Problem in Collaborative Filtering Hilmi Yıldırım and Mukkai S. Krishnamoorthy Rensselaer Polytechnic.
Information Design Trends Unit Five: Delivery Channels Lecture 2: Portals and Personalization Part 2.
Collaborative Filtering via Euclidean Embedding M. Khoshneshin and W. Street Proc. of ACM RecSys, pp , 2010.
Personalization Services in CADAL Zhang yin Zhuang Yuting Wu Jiangqin College of Computer Science, Zhejiang University November 19,2006.
Online Evolutionary Collaborative Filtering RECSYS 2010 Intelligent Database Systems Lab. School of Computer Science & Engineering Seoul National University.
Experimental Study on Item-based P-Tree Collaborative Filtering for Netflix Prize.
Item-Based Collaborative Filtering Recommendation Algorithms Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl GroupLens Research Group/ Army.
Reputation-aware QoS Value Prediction of Web Services Weiwei Qiu, Zhejiang University Zibin Zheng, The Chinese University of HongKong Xinyu Wang, Zhejiang.
The Wisdom of the Few Xavier Amatrian, Neal Lathis, Josep M. Pujol SIGIR’09 Advisor: Jia Ling, Koh Speaker: Yu Cheng, Hsieh.
Collaborative Deep Learning for Recommender Systems
Recommender System Wenxin Zhao 2014/04/04 CS548 Showcase Worcester Polytechnic Institute.
Collaborative Filtering - Pooja Hegde. The Problem : OVERLOAD Too much stuff!!!! Too many books! Too many journals! Too many movies! Too much content!
ItemBased Collaborative Filtering Recommendation Algorithms 1.
Hao Ma, Dengyong Zhou, Chao Liu Microsoft Research Michael R. Lyu
COMP423 Intelligent Agents. Recommender systems Two approaches – Collaborative Filtering Based on feedback from other users who have rated a similar set.
Trust-aware Recommender Systems
Mining Utility Functions based on user ratings
Recommenders for Information Seeking Tasks: Lessons Learned
ITEM BASED COLLABORATIVE FILTERING RECOMMENDATION ALGORITHEMS
Date: 2012/11/15 Author: Jin Young Kim, Kevyn Collins-Thompson,
Presentation transcript:

Using a Trust Network To Improve Top-N Recommendation Mohsen Jamali, Martin Ester Simon Fraser University Vancouver, Canada ACM RecSys 2009 Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Outline Introduction Collaborative Filtering Approaches User based CF Item based CF Trust based Approaches Random Walk Combined Experiments Conclusion Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Introduction Tasks of Recommender Top-N Recommendation Predicting an unknown rating Recommending a list of items Top-N Recommendation Given a user u recommend a list of items Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Collaborative Filtering for Top-N Recommendation Item-based CF for top-N recommendation [Deshpande 2004] Binary rating matrix Items: vectors in the user space. Top similar items for each item purchased by the user Most frequent items  top-N recommended items Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Collaborative Filtering for Top-N Recommendation User-based CF for top-N recommendation Very few in the literature [McLaughlin 2004] used as baseline Find Similar users to user u according to Pearson correlation. Aggregate the items rated by similar users to compute top-N recommendation Most frequent items [McLaughlin 2004]. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Using Trust Network for Top-N Recommendation Issues with CF Requires Enough Ratings Cold Start Users Cold Start Items Social Networks Emerged Recently Independent source of information Motivations of Trust-based RS Social Influence: users adopt the behavior of their friends Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation TrustWalker 2 4 5 4 3 2 item i 4 3 1 3 4 5 1 5 3 ? 2 4 2 4 3 1 user u 3 2 5 4 1 3 5 2 3 2 5 3 1 3 4 2 3 1 4 4 2 [Jamali 2009] Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation TrustWalker 2 4 5 4 3 2 item i 4 3 1 3 4 5 1 5 3 ? 2 4 2 4 3 1 user u 3 2 5 4 Φ 1 3 5 2 3 2 5 3 1 3 4 2 3 1 4 4 2 [Jamali 2009] Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation TrustWalker 2 4 5 4 3 2 item i 4 3 1 3 4 5 1 5 3 ? 2 4 2 4 3 1 user u 3 2 5 4 1 3 5 1-Φ 2 3 2 5 3 1 3 4 2 3 1 4 4 2 [Jamali 2009] Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Random Walk Approach Extension of Trust Walker [Jamali 2009] The random walk stops at a certain user v. All items rated by v but not by u will be considered as recommended items Several random walks. Rank items by aggregate rating according to different random walks. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Random Walk Approach (cont) Single Random Walk Start from the user u. At each user v, with probability Φu,v,k stops and returns items rated by v. With 1- Φu,v,k continue the random walk We consider the current step k of random walk to avoid noisy data. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Combined Approach When a user u trusts another users v, it does not necessarily mean that they rate the same items. Users who are similar according to Pearson correlation are more likely to have one more item in common. Using Leave-one-out, CF may beat Random Walk based Approach by recommending the withheld item. The list recommended by random walk approach may not contain the exact withheld item Recommended items could still be related to the user Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Combined Approach Recommended Movies Rated Movies Withheld Movie Images from www.imdb.com Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Combined Approach K2 trusted users K1 similar users Find top k1 similar users and top k2 trusted users for u. Find the top-N items according to each set of users Merge the two sets of items to have one set of N items. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Combined Approach (cont) Finding Top k1 Similar Users Similar to CF approaches Finding Top k2 Trusted Users Breadth First Search Random Walk Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Combined Approach (cont) Aggregated rating for an item i CFu , TRu : items recommended using similar (trusted) users of u : aggregate rating according to k2 trusted users : aggregate rating according to k1 similar users Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Experiments We use Epinions.com Dataset 49k users (24k cold start) 104k items 575k ratings, 508k trust relations. N=100 Evaluation Metric: Recall L: number of queries. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Comparison Partners CF-User: User based Collaborative Filtering Approach. CF-Item: Item based Collaborative Filtering Approach. TrustWalkerList D2: RandomWalk approach with maxDepth=2. TrustWalkerList D2-pure: The random Walk approach ignoring similarities. TrustWalkerList D3: RandomWalk approach with maxDepth=3. Trust-CF (k1=k2): The combined approach with k1 = k2. Trust-CF (k1=70). k1 is fixed. The top k2 trusted users are computed using BFS. Trust-CF-RW : same as Trust-CF (k1=70), but the top k2 trusted users are computed using a random walk approach. Trust-CF Weighted. Combined approach with a fixed k1 and weighted merge of results of CF and trust-based approach. The trusted neighborhood is computed using BFS. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Experimental Results Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Experimental Results Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Experimental Results Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Conclusion Future Work Addressing top-N Recommendation Exploit both trust network and rating profiles Experiments demonstrate that exploiting the social network improves the recommendations. Future Work More sophisticated combined models Better suitable evaluation metrics Distributed rating repositories Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Thank you! Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

References [Deshpande 2004] M. Deshpande and G. Karypis. Item based top-n recommendation algorithms. ACM Transactions on Information Systems, 22:143–177, 2004. [Golbeck 2005] J. Golbeck. Computing and Applying Trust in Web-based Social Networks. PhD thesis, University of Maryland College Park, 2005. [Goldberg 1992] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 1992. [Jamali 2009] M. Jamali and M. Ester. Trustwalker: A random walk model for combining trust- based and item-based recommendation. In KDD’09: The 15th ACM SIGKDD conference on Knowledge Discovery andData Mining, 2009. [Karypis 2001] G. Karypis. Evaluation of item-based top-n recommendation algorithms. In CIKM ’01: Proceedings of the tenth international conference on Information and knowledge management, pages 247–254, New York, NY, USA, 2001. [Kim 2007] H.-N. Kim, A.-T. Ji, H.-J. Kim, and G.-S. Jo. Error-based collaborative filtering algorithm for top-n recommendation. In The Joint International Conferences on Asia-Pacific Web Conference and Web-Age Information Management (APWeb/WAIM), pages 594–605, Huang Shan, China, June 2007. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

References (cont) [Kwon 2008] Y. Kwon. Improving top-n recommendation techniques using rating variance. In RecSys’08: Proceedings of the 2008 ACM conference on Recommender systems, pages 307–310, New York, NY, USA, 2008. [Massa 2007] P. Massa and P. Avesani. Trust-aware recommender systems. In RecSys’07: ACM Recommender Systems Conference, USA, 2007. [McLaughlin 2004] M. R. McLaughlin and J. L. Herlocker. A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In SIGIR ’04: Proceedings of the 27th international ACM SIGIR conference on Information Retrieval, pages 329–336, New York, NY, USA, 2004. [Richardson 2002] M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In KDD’02: The 8th ACM SIGKDD conference on Knowledge Discovery andData Mining, 2002. [Sarwar 2001] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW’01: 10th International World Wide Web Conference, 2001. Mohsen Jamali. Using Trust Networks to Improve Top-N Recommendation

Using Collaborative Filtering for Top-N Recommendation User-based CF for top-N recommendation Find top K similar users to u  Nu. Aggregate the list of items rated by all vє Nu. Similarity measure = Revised Pearson Correlation [4]

Using Collaborative Filtering for Top-N Recommendation Item-based CF for top-N recommendation Consider items rated highly by u  I’u. Find top K similar items to all items jєI’u. Aggregate these items to compute top-N items Similarity measure = Revised Pearson Correlation

Combined Approach (cont) Finding k2 trusted users: Breadth First Search Random Walk Perform several random walks to find k2 users. Estimated rating for an item would be mean of ratings expressed by trusted friends on item i.

Experimental Results – Cold Start Users

Experimental Results – Cold Start Users

Experimental Results – Cold Start Users

Experimental Results Mixture weights are not tuned