Exploring social influence via posterior effect of word of-mouth


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Exploring social influence via posterior effect of word of-mouth

  1. 1. Exploring Social Influence via Posterior Effect of Word-of-Mouth Recommendatio ns
  2. 2. ABSTRACT  the posterior effect of word-of-mouth recommendations: whether or not word-of-mouth recommendations can influence users’ posterior evaluation on the products or services recommended to them.  word-of-mouth recommendations directly prompt the posterior evaluation of receivers.  a method for investigating users’ social influence.
  3. 3. CONTRIBUTION     This paper finds a counter-intuitive phenomenon that word-of-mouth recommendations are correlated to a raise in users’ posterior evaluation. We prove, for the first time, that word-of-mouth recommendations can significantly prompt users’ posterior evaluation. We propose a framework to quantitatively measure individuals’ social influence by examining the number of their followers and their sensitivity of discovering good items. We develop a method for identifying influential friends with strong social influence.
  4. 4. PRELIMINARY STUDY 1. Data collection  Douban and Goodreads
  5. 5. If a user rates an item that has been recommended by anyone he/she follows, we consider it as a rating with word-of-mouth recommendation. Otherwise, it is a rating without word-of-mouth recommendation .
  6. 6. 2. Higher ratings on items with recommendations
  7. 7. an individual is more likely to present a high rating to an item with a wordof-mouth recommendation, compared with an item without. a word-of-mouth recommendation is correlated to a raise in user posterior evaluation .
  8. 8. an item recommended at least once has a much larger collection count than an item without any recommendation, and the collection count of an item is strongly related to its recommendation count. the mean rating of items with at least one recommendation is higher than that of items without recommendations.
  9. 9. POSTERIOR EVALUATION MODELS  two 1. 2. possible models : a word-of-mouth recommendation and a higher rating of an item both attribute to certain common but unknown factors; a word-of-mouth recommendation directly influences the rater to present a higher rating.
  10. 10. MODEL VERIFICATION 1. Designing statistical hypothesis tests recommender’s rating, r’ a) r’ depends on c symmetrically as r in the original independent model; b) m’ fully depends on r’ due to a common practice that whether or not a user recommends an item is determined by his/her rating on this item.   examining the conditional independence between m’ and r given r’
  11. 11.  we discretize the real-valued r’ into distinct values in two ways:   For the equal-interval partitioning, the values of r’ in each partition are confined within a fixed small interval. For the equal-frequencies partitioning, each partition has a fixed number of data points.  p(m’|r’) and p(r|r’)  p(r|m’=0,r’) p(r|m’=1,r’)  The location t -test and the KolmogorovSmirnov test
  12. 12. 2. Results of statistical hypothesis tests
  13. 13.  1. Slice data into partitions. All data points in each partition share a unique r’ value.  2. Divide each partition into two samples, one with m’= 1 and the other with m’=0.  3. In each partition, employ the two-sample t -test and a two-sample Kolmogorov-Smirnov test to examine the null hypothesis, i.e. the two samples are identically distributed.  4. Report the percentage of partitions where statistical hypothesis tests reject the null hypothesis.
  14. 14. IDENTIFYING INFLUENTIAL FRIENDS  Two kinds of factors, namely, the social positions of users in the friendship network and their personal characteristics independent on other individuals. 1. PageRank and LeaderRank 2. collection size, collection frequency (number of items collected per day) and sensitivity.
  15. 15. It is necessary to combine the social position of individuals and their personal characteristics when identifying the influential friends for social recommendations.
  16. 16. CONCLUSIONS  word-of-mouth recommendations can not only improve the prior expectation of users, who receive the recommendations, on the recommended items, but also raise their posterior evaluation.  a method for investigating a user’s social influence.
  17. 17. Maximizing Product Adoption in Social Networks
  18. 18. ABSTRACT  it is important to distinguish product adoption from influence.  We adapt the classical Linear Threshold (L T) propagation model by defining an objective function that explicitly captures product adoption, as opposed to influence.
  19. 19.  maximize the number of product adoptions  contributions : 1. We present an intuitive model called LT-C , for LT with Colors 2. We study two types of networks movies networks and music networks 3. we found that there is a positive correlation between the number of initiators and the final spread.
  20. 20. PROPOSED FRAMEWORK G =(V;E;W)  a matrix R of Users * Products 1. LT -C Model
  21. 21. 2. Maximizing Product Adoption
  22. 22.  3.    4.    Choosing Optimal Seed Set Monte Carlo simulation CELF algorithm Learning Model Parameters Edge Weights Ratings Matrix Node Parameters λ and μ
  23. 23. MODEL EVALUATION   Classical LT. Linear Threshold model. LT-C. Our proposed model. LT Ratings. Our proposed model without Tattle nodes. That is, all the nodes who are influenced adopt the product. LT Tattle. Our proposed model without any ratings. That is, all the nodes in Adopt state are assumed to rate the item as r-max ,as do those in Promote state, while users in Inhibit state rate r-min.
  24. 24. 1. Adoption of Movies
  25. 25. 2. Adoption of Music
  26. 26. CONCLUSIONS a propagation model called LT-C model  We formalized the problem of adoption maximization as distinguished from influence maximization and showed that it is NP-hard.
  27. 27. Thanks