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Social Recommender Systems Ido Guy, David Carmel IBM Research-Haifa, Israel WWW 2011, March 28 th -April 1 st , Hyderabad, India
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
Web 2.0 and Social Media ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Top 10 Website (Alexa.com, 3/2011) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Social Overload ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Social Overload ,[object Object],[object Object]
Social Recommender Systems ,[object Object],[object Object],[object Object],[object Object]
Recommender Systems and Social Media ,[object Object],[object Object],[object Object],Recommender Systems Social Media Social media introduces new types of data and metadata that can be leveraged by RS (tags, comments, votes, explicit social relationships)  RS can significantly impact the success of social media, ensuring each user is present with the most relevant items that suits her personal needs
Real-World Examples
Real-World Examples
Real-World Examples
Real-World Examples
Real-World Examples
Real-World Examples ,[object Object],[object Object]
Fundamental Recommendation Approaches IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
Fundamental Recommendation approaches  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommendation techniques ( Burke,2002 ) Generate a classifier based on  u ’s ratings, use it to classify new items Rating from  u  to items Features of items Content-based Infer a match between items and  u ’s needs User needs Features of items Knowledge-based Identify similar users, extrapolate from their rating Demographic information about  u Demographic information about users Demographic Identify similar users, extrapolate from their rating Rating from  u  to items User-item matrix CF Process Input Background Technique
Collaborative Filtering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
User based CF algorithm ,[object Object],Jon’s taste is similar to both Chris and Alice  tastes    Do not recommend Superman to Jon  Shall we recommend Superman for John? ? Like Like Jon Dislike Like  Like Chris Like Dislike ? Bob Dislike Like Like Alice Superman Snow-white Shrek
User based CF algorithm (cont) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cosine based similarity between users Pearson based similarity between users
CF - Practical challenges ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Item-Based Nearest Neighbor Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Bob dislikes Snow-white (which is similar to Shrek)    do not recommend Shrek to Bob ? Like Like Jon Dislike Like  Like Chris Like Dislike ? Bob Dislike Like Like Alice Superman Snow-white Shrek
Dimensionality Reduction  Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Singular Value Decomposition (SVD) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Hybrid recommendation methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object]
Content Recommendation IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
Video Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Video Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
News Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
News Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object]
News Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Enhancing CF with Friends ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Blog Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Mixed Social Media Item Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],Personalized Recommendation of Social Software Items based on Social Relations [Guy et al., RecSys ’09]
Mixed Social Media Item Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object]
Mixed Social Media Item Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object]
Mixed Social Media Item Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Tag-based Movie Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary of Key Points ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object]
Tag Recommendation IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
Tag Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
Tag Recommendation Approaches ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Flickr’s tag recommender (Sigurbjornsson WWW2008 )
Content-based tag recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Graph based approaches ,[object Object],[object Object],[object Object],[object Object],[object Object],w  – a weight vector over nodes A – a row-stochastic matrix of the graph p   - preference vector over the nodes ,[object Object]
Tag Recommendations in Folksonomies, Jaeschke07 ,[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
People Recommendation IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
Social Matching ,[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending People to Connect with ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending People to Connect with ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending People to Connect with ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],4 Algorithms Compared
Recommending People to Connect with ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending People to Connect with ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],degree distribution Betweenness delta
Recommending People to Connect with ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending People to Follow ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending People to Follow ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending People to Follow ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Expertise Location ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Community Recommendations IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
Recommending Similar Communities ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],at time of experiments L1-Norm L2-Norm Log-Odds Salton (IDF) Pointwise Mutual-Info: Pos. Correlations  Pointwise Mutual-Info: Pos. and Neg. Correlations
Recommending Similar Communities ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Personalized Community Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Personalized Community Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Personalized Community Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object]
Personalized Community Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Personalized Community Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommendations for Groups IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
Recommendation for a group rather than individuals ,[object Object],[object Object],[object Object],[object Object]
Issues in recommendation for groups How to support the process of arriving at a final decision Negotiation may be required. Members decide which recommendation (if any) to accept Aggregation methods Aggregating preferences/results must be applied System generates recommendations Benefits/drawback for the group/system Members can examine each other  Members specify their preferences General issues Differences from individuals Phase of recommendation
Explicit specification of preferences: MusicFX ( McCarthy, CSCW 2000) Hate   Don’t mind Love Alternative rock :  1  2  3  4  5 Hot country: 1  2  3  4  5 50’oldies 1  2  3  4  5 …90 more genres
Collaborative speciation of preferences in the Travel Decision Forum [Jameson 04]
Collaborative specification - Advantages ,[object Object],[object Object],[object Object],[object Object],[object Object]
Aggregating Preferences ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Aggregation Methods [Berkovsky, RecSys ’10]
Group recommendations ..  Baltrunas et a.l, RecSys2010 ,[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Cold Start Problem IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
The Cold Start Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],CF CB CF CB
The Cold Start Problem of New Items ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Recommend Y to B Recommend Y to A CB CF+CB User A Item X Item Y User B Similar Likes Similar User A Item X Item Y Likes Similar
The Cold Start Problem of New Items ,[object Object],[object Object]
The Cold Start Problem for New Users  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Cold Start Problem for New Users  ,[object Object],[object Object],[object Object],[object Object]
The Cold Start Problem for New Users  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cold Start for Tag-based Recommenders ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object]
Trust and Distrust IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
Social relations based recommendation ,[object Object],[object Object],[object Object],[object Object]
 
Trust Enhanced Recommendation ,[object Object],[object Object],[object Object]
Social trust graph User-item rating matrix Problem Definition
TidalTrust: Accumulate recommendation from trusted people only (Golbeck06)
Integrating Trust with CF ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inferring Trust The Goal: Select two individuals - the  source  and  sink  - and recommend to the source how much to trust the sink . Sink Source
Trust computations ,[object Object],[object Object]
Reputation ,[object Object],[object Object],[object Object]
What about Distrust? (Victor 2009) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Trust in Recommendation  (by explanations) Good explanations could help inspire user trust and loyalty, increase satisfaction, make it quicker and easier for users to find what they want, and persuade them to try or purchase a recommended item MoviExplain: A Recommender System with Explanations   (Symeonidis09)
Explanation Aims: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Explanation types ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
TagSplanations (Vig09) ,[object Object],[object Object],[object Object],Rushmore Rear Window
Social-based Explanation (Guy09)
References ,[object Object],[object Object],[object Object],[object Object],[object Object]
Social Recommender Systems in the Enterprise IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
Social Media in the Enterprise ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
IBM Lotus Connections – Enterprise Social Software
Social Recommenders in the Enterprise ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending Content to Create ,[object Object],[object Object],[object Object],[object Object],[object Object]
Increasing Engagement in Enterprise Social Media ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Stranger Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Stranger Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object]
Temporal Aspects in Social Recommendation IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
The Time Factor in RS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Temporal CF ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Temporal Diversity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Temporal Diversity ,[object Object],[object Object],[object Object],[object Object],[object Object]
Temporal User Profile ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Time-based Evaluation in SRS Google Reader News Recommendation Network effects of people recommendation Increasing engagement of new users
Future Directions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object]
Social Recommendation over Activity Streams IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
Activity streams and the Real-time Web ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],External (aggregated) Hetrogenous Asymmetric (follow) FriendFeed Internal Hetrogenous Symmetric (friends) Facebook Internal Homogenous (status updates) Asymmetric (follow) Twitter
Utilizing the Stream for Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Enhancing News Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Enhancing Movie Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object]
Personalized Filtering of the Stream ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending Twitter URLs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommending Twitter URLs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Topic-based Twitter Filtering ,[object Object]
Topic-based Twitter Filtering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object]
Evaluation Methods IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
Evaluation goals ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Offline Evaluation ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Netflix Challenge (Bernett 07) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evaluating Prediction Accuracy –  the user opinions/ratings over items Root Mean Squared Error (RMSE)   the most popular metric used in evaluating accuracy of predicted ratings A popular alternative:  Mean Absolute Error (MAE) ,[object Object],[object Object]
Measuring Ranking Accuracy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evaluating   top-N recommendation   (Cremonesi, RecSys2010) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Off-line evaluation of a Tag Recommendation System using social bookmarks ( Carmel, CIKM’09) ,[object Object],[object Object],[object Object],[object Object],[object Object],(d1,u1):  t1,  t2 , t3,  t4 , t5 ,t6, t7, t8,   t 9 , t10 … (d2,u2):  t1 , t2,  t3 ,  t4 , t5 , t6 , t7, t8, t9, t10 … (d3,u3):  t1, t2, t3, t4, t5 ,t6, t7, t8,  t9 , t10 …
User Studies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Questions to measure subjective constructs
User studies – Pros and Cons ,[object Object],[object Object],[object Object],[object Object],[object Object]
Online Evaluation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object]
Open Issues and Research Challenges IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
Open Issues and Research challenges ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Questions? ,[object Object],[object Object]

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Social Recommender Systems Tutorial - WWW 2011

  • 1. Social Recommender Systems Ido Guy, David Carmel IBM Research-Haifa, Israel WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 2.
  • 3. Introduction IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 15.
  • 16. Fundamental Recommendation Approaches IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
  • 17.
  • 18. Recommendation techniques ( Burke,2002 ) Generate a classifier based on u ’s ratings, use it to classify new items Rating from u to items Features of items Content-based Infer a match between items and u ’s needs User needs Features of items Knowledge-based Identify similar users, extrapolate from their rating Demographic information about u Demographic information about users Demographic Identify similar users, extrapolate from their rating Rating from u to items User-item matrix CF Process Input Background Technique
  • 19.
  • 20.
  • 21.
  • 22. Cosine based similarity between users Pearson based similarity between users
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29. Content Recommendation IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45. Tag Recommendation IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
  • 46.
  • 47.  
  • 48.
  • 49. Flickr’s tag recommender (Sigurbjornsson WWW2008 )
  • 50.
  • 51.
  • 52.
  • 53.
  • 54. People Recommendation IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 55.
  • 56.
  • 57.
  • 58.
  • 59.
  • 60.
  • 61.
  • 62.
  • 63.
  • 64.
  • 65.
  • 66.
  • 67. Community Recommendations IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 68.
  • 69.
  • 70.
  • 71.
  • 72.
  • 73.
  • 74.
  • 75.
  • 76. Recommendations for Groups IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
  • 77.
  • 78. Issues in recommendation for groups How to support the process of arriving at a final decision Negotiation may be required. Members decide which recommendation (if any) to accept Aggregation methods Aggregating preferences/results must be applied System generates recommendations Benefits/drawback for the group/system Members can examine each other Members specify their preferences General issues Differences from individuals Phase of recommendation
  • 79. Explicit specification of preferences: MusicFX ( McCarthy, CSCW 2000) Hate Don’t mind Love Alternative rock : 1 2 3 4 5 Hot country: 1 2 3 4 5 50’oldies 1 2 3 4 5 …90 more genres
  • 80. Collaborative speciation of preferences in the Travel Decision Forum [Jameson 04]
  • 81.
  • 82.
  • 84.
  • 85.
  • 86. The Cold Start Problem IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 87.
  • 88.
  • 89.
  • 90.
  • 91.
  • 92.
  • 93.
  • 94.
  • 95. Trust and Distrust IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
  • 96.
  • 97.  
  • 98.
  • 99. Social trust graph User-item rating matrix Problem Definition
  • 100. TidalTrust: Accumulate recommendation from trusted people only (Golbeck06)
  • 101.
  • 102. Inferring Trust The Goal: Select two individuals - the source and sink - and recommend to the source how much to trust the sink . Sink Source
  • 103.
  • 104.
  • 105.
  • 106. Trust in Recommendation (by explanations) Good explanations could help inspire user trust and loyalty, increase satisfaction, make it quicker and easier for users to find what they want, and persuade them to try or purchase a recommended item MoviExplain: A Recommender System with Explanations (Symeonidis09)
  • 107.
  • 108.
  • 109.
  • 111.
  • 112. Social Recommender Systems in the Enterprise IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 113.
  • 114. IBM Lotus Connections – Enterprise Social Software
  • 115.
  • 116.
  • 117.
  • 118.
  • 119.
  • 120.
  • 121. Temporal Aspects in Social Recommendation IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 122.
  • 123.
  • 124.
  • 125.
  • 126.
  • 127. Time-based Evaluation in SRS Google Reader News Recommendation Network effects of people recommendation Increasing engagement of new users
  • 128.
  • 129.
  • 130. Social Recommendation over Activity Streams IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India
  • 131.
  • 132.
  • 133.
  • 134.
  • 135.
  • 136.
  • 137.
  • 138.
  • 139.
  • 140.
  • 141. Evaluation Methods IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
  • 142.
  • 143.
  • 144.
  • 145.
  • 146.
  • 147.
  • 148.
  • 149.
  • 150. Questions to measure subjective constructs
  • 151.
  • 152.
  • 153.
  • 154. Open Issues and Research Challenges IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
  • 155.
  • 156.

Notes de l'éditeur

  1. The sites in the top sites lists are ordered by their 1 month alexa traffic rank. The 1 month rank is calculated using a combination of average daily visitors and pageviews over the past month. The site with the highest combination of visitors and pageviews is ranked #1.
  2. Facebook – updated March 2011 Twitter – June 2010 YouTube – May 2010
  3. To mention that table entries can be filled by implicit feedback
  4. Outside the box – since similar users can recommend surprising items compared to content-based approaches which is limited to items that fit the user profile
  5. What a Difference a Group Makes:Web-Based Recommendations for Interrelated Users (Mason & Smith)
  6. Explaining collaborative filtering
  7. Tagsplanations: explaining recommendations using tags, Vig, IUI09
  8. User Evaluation Framework of Recommender Systems, Chen (SRS 2010)