Difference between revisions of "Konstas et al. SIGIR 2009"
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== Summary == | == Summary == | ||
| − | This [[Category::paper]] | + | This [[Category::paper]] applies [[UsesMethod::Random walk with restart]] as a method of [[AddressesProblem::Content recommendation|content recommendation]] in social network systems. |
As it is in the context of social network, naturally and effectively, a graph based algorithm such as [[UsesMethod::Random walk with restart]] can be considered to perform the [[AddressesProblem::Content recommendation|recommendation]] task. | As it is in the context of social network, naturally and effectively, a graph based algorithm such as [[UsesMethod::Random walk with restart]] can be considered to perform the [[AddressesProblem::Content recommendation|recommendation]] task. | ||
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This [[Category::paper]] argues that the extra knowledge provided by the users' social activity, such as the social annotation and friendships inherent in the social graph established among users, items and tags, can improve the performance of a recommendation system using methods such as [[UsesMethod::Random walk with restart]]. | This [[Category::paper]] argues that the extra knowledge provided by the users' social activity, such as the social annotation and friendships inherent in the social graph established among users, items and tags, can improve the performance of a recommendation system using methods such as [[UsesMethod::Random walk with restart]]. | ||
| − | The | + | The argument is supported based on a series of comparison experiments between the [[UsesMethod::Random walk with restart]] model and a user-based [[Social_collaborative_filtering|collaborative filtering]] method using the Pearson Correlation similarity. The results show that the graph model system benefits from the additional information embedded in social knowledge. In addition, the graph model outperforms the standard [[Social_collaborative_filtering|collaborative filtering]] method. |
== The dataset == | == The dataset == | ||
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The UU sub-matrix consists the average user playcount. This is crucial because the UTr sub-matrix may contain very large values and thus the binary values in UU will be suppressed. | The UU sub-matrix consists the average user playcount. This is crucial because the UTr sub-matrix may contain very large values and thus the binary values in UU will be suppressed. | ||
| − | In the case of the UTg sub-matrix, the associated tags for each user have been collected based on popularity. | + | In the case of the UTg sub-matrix, the associated tags for each user have been collected based on popularity. An exponential decay function is applied to the values of the tags of each user in the matrix, where the most popular tag gets the average user's playcount. The same process was performed for the TrTg sub-matrix accordingly. |
The full social graph look like: | The full social graph look like: | ||
| Line 48: | Line 48: | ||
== Related papers == | == Related papers == | ||
*[[RelatedPaper:: Bu et al. MM 2010|Music Recommendation by Unified Hypergraph: Combining Social Media Information and Music Content]] | *[[RelatedPaper:: Bu et al. MM 2010|Music Recommendation by Unified Hypergraph: Combining Social Media Information and Music Content]] | ||
| − | Interesting article also leverages the social media information and the music content itself to | + | Interesting article also leverages the social media information and the music content itself to recommend music. |
*[[RelatedPaper:: Yildirim and Krishnamoorthy MM RecSys 2008|A random walk method for alleviating the sparsity problem in collaborative filtering]] | *[[RelatedPaper:: Yildirim and Krishnamoorthy MM RecSys 2008|A random walk method for alleviating the sparsity problem in collaborative filtering]] | ||
Another recommendation algorithm using random walk. | Another recommendation algorithm using random walk. | ||
| − | *[[RelatedPaper:Yuan et al. RecSys 2009|Augmenting collaborative recommender by fusing explicit social relationships]] | + | *[[RelatedPaper: Yuan et al. RecSys 2009|Augmenting collaborative recommender by fusing explicit social relationships]] |
Two principal methods to integrate explicit social relationships into traditional CF methods: the weighted-similarity fusion and the graph fusion. | Two principal methods to integrate explicit social relationships into traditional CF methods: the weighted-similarity fusion and the graph fusion. | ||
== Study plan == | == Study plan == | ||
* Web page: [[Random walk with restart]] | * Web page: [[Random walk with restart]] | ||
| − | * Slides: [ | + | * Slides: [http://www.cs.cmu.edu/~wcohen/collab-filtering-tutorial.ppt Collaborative Filtering: A Tutorial] |
| + | * Article: [http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient Pearson correlation] | ||
Latest revision as of 08:37, 2 October 2012
Contents
Citation
Ioannis Konstas, Vassilios Stathopoulos, and Joemon M. Jose. On social networks and collaborative recommendation. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’09, pages 195–202, New York, NY, USA, 2009. ACM.
Online Version
Summary
This paper applies Random walk with restart as a method of content recommendation in social network systems.
As it is in the context of social network, naturally and effectively, a graph based algorithm such as Random walk with restart can be considered to perform the recommendation task.
This paper argues that the extra knowledge provided by the users' social activity, such as the social annotation and friendships inherent in the social graph established among users, items and tags, can improve the performance of a recommendation system using methods such as Random walk with restart.
The argument is supported based on a series of comparison experiments between the Random walk with restart model and a user-based collaborative filtering method using the Pearson Correlation similarity. The results show that the graph model system benefits from the additional information embedded in social knowledge. In addition, the graph model outperforms the standard collaborative filtering method.
The dataset
The source of dataset comes from last.fm - a Web 2.0 website. As introduced in the dataset, there are three components serving as the vertices in the graph - users, music tracks and tags, deriving relation matrices as User-Track(UTr), User-Tag(UTg) and Track-Tag(TrTg).
The UTr sub-matrix consists of the playcount of every track by each user, i.e. the number of times it has been listened to.
The UU sub-matrix consists the average user playcount. This is crucial because the UTr sub-matrix may contain very large values and thus the binary values in UU will be suppressed.
In the case of the UTg sub-matrix, the associated tags for each user have been collected based on popularity. An exponential decay function is applied to the values of the tags of each user in the matrix, where the most popular tag gets the average user's playcount. The same process was performed for the TrTg sub-matrix accordingly.
The full social graph look like:
Experiment results and observations
The experiment result is concluded as following table:
There are several observations from the result worth noticing:
- The addition of social network and social tagging information increases the number of relevant retrieved (num_rel_ret) tracks in the RWR method.
- There is also a notable decrease in MAP and Precision at high ranks and in parallel to num_rel_ret increase and Precision at lower ranks. This tendency accounts for the fact that with the progressive addition of social knowledge the method retrieves more tracks thus effectively increases its recall but at lower ranks (e.g P@200), causing a harm to its precision.
- the RWR method with the UTrUTg graph retrieves statistically significantly more relative tracks than the RWR method with the UTrUU graph (and also has higher P@200 and P@1000) compared to the simple RWR method.
The above three observation highlights the benifits of the social graph.
- Using the same sub-matrix as the input, the RWR method always outperforms the CF method. This supports the argument that the RWR method is more effective than the CF method.
- Using the CF method, the addition of friendships and social tagging information deteriorates the performance increasingly. This supports the argument that the memory based CF method cannot provide with adequate non-trivial mechanisms to incorporate social knowledge.
Related papers
Interesting article also leverages the social media information and the music content itself to recommend music.
Another recommendation algorithm using random walk.
Two principal methods to integrate explicit social relationships into traditional CF methods: the weighted-similarity fusion and the graph fusion.
Study plan
- Web page: Random walk with restart
- Slides: Collaborative Filtering: A Tutorial
- Article: Pearson correlation
