Do not blame it on the algorithm - An empirical assessment of multiple recommender systems and their impact on content diversity
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Presentation at IC2C2, Cologne, 11-7-2017
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Do not blame it on the algorithm - An empirical assessment of multiple recommender systems and their impact on content diversity
- 1. Do not blame it on the algorithm - An empirical assessment of multiple recommender systems and their impact on content diversity Möller, Trilling, Helberger, van Es The Amsterdam Personalised Communication Project
- 2. Does algorithmic recommendation lead to less diversity? 2
- 3. News recommendation algorithms Optimize sets of recommended news articles by sorting them according to • Popularity • Collaborative filtering • Semantic match 3
- 4. Diversity Functional approach: Diversity has a different meaning/ significance depending on which value or objective it is connected to 4
- 5. Democratic participation Ratio of news/public affairs Different political/ideo logical viewpoints Tolerance Ratio of content dedicated to ethnic, linguistic, national groups Different languages Autonomy Choice between different formats, topics, genres, sources Little variance, in the sense of distance from personal preferences Deliberation Reconcillary tone, share of articles presenting various perspectives Diversity of emotions, range of different authors, sources Contestation Minority voices Share of content that is purposefully biased 5
- 6. Democratic participation Ratio of news/public affairs Different political/ideo logical viewpoints Tolerance Ratio of content dedicated to ethnic, linguistic, national groups Different languages Autonomy Choice between different formats, topics, genres, sources Little variance, in the sense of distance from personal preferences Deliberation Reconcillary tone, share of articles presenting various perspectives Diversity of emotions, range of different authors, sources Contestation Minority voices Share of content that is purposefully biased 6
- 7. Research question Which algorithmic setting in news recommender system produces the largest and smallest amount of diversity on the different dimensions of diversity? 7
- 8. Methods Data: 1000 Simulated recommendation sets in different algorithmic settings based of data on Volkskrant.nl published between 19.9.2016 and 26.9 2016, N=21,973 articles Benchmark: Recommendation by the human editor 8
- 9. 9
- 10. Methods Operationalization (Preliminary): Diversity for democratic participation: Overlap in topic; Diversity for autonomy: Overlap in section of the newspaper, Diversity for delibartion: Overlap in tone and emotions 10
- 11. How to measure overlap Naïve approach: (1) map: same feature (topic) 1; different feature (topic) 0 (2) sum In a recommendation set of three articles, we can get the scores {0; 1; 2; 3} Example: An article about sport leads to the recommendation set {politics; sport; sport} score 2 11
- 12. Results Benchmark Popularity Col. filter Semantic Participation 1.237 1.094 1.286 1.251 Autonomy 0.38 0.29 0.38 0.43 Deliberation 0.603 0.512 0.585 0.654 12
- 13. How to measure overlap But this naïve approach oversimplifies. Better: Instead of binary [0/1] have [0;1] interval Distance between topics as feature; not presence as feature 13
- 16. (1) Use MDS to represent each topic t by coordinates (x,y) in two-dimensional space (2) Each document D is represented by vector of topic weights 3) Calculate topic dissimilarity matrix M (50x50) 16 Preparing the matrices
- 17. (1) We multiply M with a and , for document 1 and 2 , resulting in two matrices: These represent the topic dissimilarity matrices weighed by the topic occurrence in the document in question. (2) We can then calculate the Euclidian distance between the two matrices using the Frobenius norm: 17 Comparing two documents
- 18. Apply to recommendation sets Given that each document in the dataset generated three recommendations, we propose to calculate several measures: the mean of the distance of each article in the reommendation set with the original article. the mean of the distances within the recommendation set 18
- 19. Results
- 20. 20
- 21. Future work Evaluate topic distance measure: crowdcoding? More diverse corpora Include minority voice diversity (Wikipedia) DART 21
- 22. Work in progress… optimize diversity in recommender systems.
- 23. Thanks Want to know more? @judith_moeller, @damian0604 Personalised-communication.net
Notas do Editor
- Functional approach: diversity is not a goal in itself but a means to and end, namely the realisation of particular values and objectives. Which values and objectives those are depends, among other, on the conceptualisation of pluralism. Arguably, there is not one conceptualisation of pluralism but several possible conceptual (liberal pluralism, deliberate pluralism, cultural pluralism, radical pluralism). Seeing that media diversity is not a value in itself but a means to achieve a particular goal, it is probably too restricted to simply measure the diversity of an information offer ipso jure. Instead, we should take into account the way people find, collect, process, engage and act upon diverse information. In other words, a complete vision of diversity also looks into the presentation (if the ways of presentation/style of writing/etc) reflect the heterogeneity for different styles, etc., , the visibility and ease of access of diverse content (including geographical access), the engagement with the content (in the form of sharing, liking, hyperlinking, commenting, etc.), etc. Pluralism and diversity can be measured in two ways: absolute (e.g. the number of news papers from different sources) and proportionate/relational (the ratio of news papers from one source in relationship to the number of news papers from another source). Typically, it is not a piece of content that is diverse, but how this content relates to other contents.
- Red circle is what was recommended to one specific user, How to optimize the recommender, mover users towards norm (0), and you want to make the circle larger