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From Mining to Understanding: The Evolution of Social Web Users
 

From Mining to Understanding: The Evolution of Social Web Users

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Presentation at Lancaster University's Faculty of Science and Technology Christmas Conference. December 2013.

Presentation at Lancaster University's Faculty of Science and Technology Christmas Conference. December 2013.

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    From Mining to Understanding: The Evolution of Social Web Users From Mining to Understanding: The Evolution of Social Web Users Presentation Transcript

    • FROM MINING TO UNDERSTANDING: THE EVOLUTION OF SOCIAL WEB USERS DR. MATTHEW ROWE SCHOOL OF COMPUTING AND COMMUNICATIONS @MROWEBOT | M.ROWE@LANCASTER.AC.UK Faculty of Science and Technology Christmas Conference Lancaster University, UK
    • Our interests develop ‘Offline’ Primary School High School University Time 1 From Mining to Understanding: The Evolution of Social Web Users Postgrad Postdoc Lecturing
    • And so too do our social networks… Offline, we develop in terms of both our interests and social networks Primary School High School University Time 2 From Mining to Understanding: The Evolution of Social Web Users Postgrad Postdoc Lecturing
    • This also happens ‘online’, on the ‘Social Web’… 3 From Mining to Understanding: The Evolution of Social Web Users
    • First, Web 1.0 4 From Mining to Understanding: The Evolution of Social Web Users
    • Then, Web 2.0… the ‘Social Web’ 5 From Mining to Understanding: The Evolution of Social Web Users
    • …to understand how people behave online …to learn how people shape their identities Why study user evolution? …to predict churners (from social networks and online communities) 6 From Mining to Understanding: The Evolution of Social Web Users …to build better recommender systems
    • Talk Outline User Lifecycles, Properties & Evolution Measures Predicting Churners 7 From Mining to Understanding: The Evolution of Social Web Users Recommending Items Conclusions
    • 8 User Lifecycles From Mining to Understanding: The Evolution of Social Web Users
    • Modelling User Evolution: Lifecycles Offline Lifecycle Periods Primary School High School University Postgrad Postdoc Lecturing Time First Action Last Action Lifecycle Periods of a potential Question-Answering System user (conjecture!) Novice Users Asking Questions Asking & Answering Questions Answering Questions In reality: do not know the labels, however we can split by equal time intervals: 1 2 3 … n Yet, users non-uniformly distribute their activity across lifecycles 1 2 3 9 From Mining to Understanding: The Evolution of Social Web Users … n
    • User Properties in Lifecycle Stages 1 2 1 #actions 3 2 = … n We divide lifetime into equal activity periods #actions Model the actions to user u by other users Model the actions by user u to other users Term s Count Model the tastes of the user 10 From Mining to Understanding: The Evolution of Social Web Users 17 Web 5 Item Mining Model the terms used by user u Semantic 4 Rating Alien Statistics 3 4* Bladerunner 5* Star Wars 4*
    • How can we track the evolution of user’s properties? Solution: use measures from information theory 11 From Mining to Understanding: The Evolution of Social Web Users
    • by computing the cross-entropy of one probability distribution with respect to another distribution from an lifecycle period, and the properties differ between time steps? How do then selecting the distribution that minimises cross-entropy. Assuming we have a probability distribution Decrease = similarity between properties (P ) formed from a given lifecycle period ([t, t0 ]), and a probability distribution (Q) from an earlier lifecycle period, then we define the cross-entropy between the distributions as follows: Evolution measure 1: Cross-Entropy X H(P, Q) = p(x) log q(x) (5) x In properties in vein User the same period sas the earlier entropy analysis, we derived the period cross-entropy for each platform’s users User Properties in period s-1 throughout their lifecycles and then derived the mean crossentropy for the 20 lifecycle periods. Figure 2 presents the 12 cross-entropies The Evolution of Social Webthe different platforms and user derived for Users From Mining to Understanding: properties. We observe that for each distribution and each
    • By using conditional entropy we can assess the information needed to describe the taste profile of a user at one time How much information is transferred previous period step (Q) using his taste profile from the from one stage (P ). to entropy A reduction in conditionalthe next?indicates that the user’s taste profile is similar to information is transferred Decrease = more that of his previous stage’s profile, while an increase indicates the converse. We define the conditional entropy of two discrete probability distributions, representing taste profiles, as: Conditional Entropy Evolution measure 2: X p(x) H(Q|P ) = p(x, y) log (5) p(x, y) x2P, y2Q We derived the conditional entropy over the 5 lifecycle User properties in period s periods in a pairwise fashion, i.e. H(P2 |P1 ), . . . , H(P5 |P4 ), and User Properties in periodof the mean conditional entropy in plotted the curve s-1 Figure 5 over each dataset’s users in the training split, also including the 95% confidence intervals to show the varia13 From tionMining tothe conditionalSocial Web Users in Understanding: The Evolution of entropies. Figure 5 indicates that
    • examine the information transfer from a prior lifecycle stage (s 1) to the current lifecycle stage (s) of the user. Now, assume that we have a random variable thatthe user’s the local How do global dynamics influence describe categories that have been reviewed at the current stage (Ys ), properties? a random variable of local categoriesglobal influence stage Decrease = more susceptible to at the previous (Ys 1 ). and a third random variable of global categories at Increase = less susceptible to global influence the previous stage (Xs 1 ), we then define the transfer entropy of one lifecycle stage to another as follows, based on the work of Schreiber measure 3: Transfer Entropy Evolution [8]: TX!Y = H(Ys |Ys 1) H(Ys |Ys 1 , Xs 1 ) (6) Using the above probability distributions we can calculate the transfer entropy based on the joint and conditional probSurprise in user properties from s-1 to s ability distributions given the values of the random variables Surprise in user properties in s when we consider all users’ properties from s-1 14 From Mining to Understanding: The Evolution of Social Web Users
    • 15 Predicting Churners via Evolution Signals ...from Online Communities From Mining to Understanding: The Evolution of Social Web Users
    • d testing, using the former in this section to examine user development e latter split forOnline Communities experiments. Datasets: our later detection Platform Time Span Post Count User Count Facebook [18-08-2007,24-01-2013] 118,432 4,745 SAP [15-12-2003,20-07-2011] 427,221 32,926 Server Fault [01-08-2008,31-03-2011] 234,790 33,285 Churner ‘Cutoff’’ Defining Lifecycle Periods For th 1500 800 1000 1 Table 1. Statistics of the online community platform datasets. 1000 500 Posts Frequency 1000 2008 2010 Time 2012 0 0 200 600 Posts Frequency 600 400 200 0 Posts Frequency order to examine how users develop over time we needed some Fault mean gment a user’s lifetime (i.e. from the first date at which they post to thet rate simila their final post) into discrete intervals. Prior work [6, 2, 5] has demonstr the cr e extent to which users develop at their own pace and thus evolve accor must s their own ‘personal clock ’ [5]. Hence, for deriving the lifecycle periods ofis u fect thin the platforms we adopted an activity-slicing approach that divid non-ch (a) Facebook (b) SAP (c) Server Fault comm er’s lifetime into 20 discrete time intervals, emulating the approach in [2], 16 th an equal proportion of activity within each period. This approach than c funct From Mining to Understanding: The Evolution of Social Web Users distrib follows: we derive the Posts per-day for the ({[ti , tj ]} with ) by first deri Figure 2: set of interval tuples datasets 2 T the to foll 2004 2006 2008 Time 2010 2009 2010 Time 2011
    • 0.8 0 0.2 0.4 ● ●●● ● ● 0.6 0.8 0.04 0.03 0.02 0.01 ● 0.10 0.15 - (b) In-degree SAP 0.05 ● 17 ● ● From Mining to Understanding: The Evolution of Social Web Users 0.10 ●●● 1 ● 0 ●● ● ●● ●●●●●●●● 0.2 Lifecycle Stages −period Cross Entropy 0.20 0.15 ● ● 0.00 0.20 0.15 0.10 0.05 1 Lifecycle Stages (a) In-degree Facebook ● 0.4 ● ●●● 0.6 0.8 ●●● 1 Lifecycle Stages - (c) In-degree Server Fault 0.06 0.6 ●● ● 0.04 0.4 ●● ● ● ● 0.02 0.2 ●●●●●●● ● ● ●● Time−period Cross Entropy ●● ●● ● −period Cross Entropy ● ● 0.00 ● Time−period Cross Entropy 0.00 0.02 0.04 0.06 0.08 0.10 Churners Non−churners ● 0 .05 h sn To s’ ss bm at Time−period Cross Entropy n −period Cross Entropy e = than churners. For the cross-entropy of users’ lexical term distributions dissimilarity with prior in-degree non-churners Cross-Entropy: we find the signals of churner andinformation to follow a similar curvature user differ from before? I.e. how do users who contact a given(converging on a limit with a decaying rate) but with di↵erent magnitudes. ● ● -
    • ●●● ● ●● ●● ● ● ● ● ● 0.5 3.0 ●●● ● ● ● Co Co 0.2 Co ● ● ● Cross-Entropy: dissimilarity with community out-degree information (a) In-degree - (b) In-degree - (c) In-degree 0 0.2 0.4 0.6 0.8 1 0 0.2 Lifecycle Stages 0.4 0.6 0.8 1 0 0.2 Lifecycle Stages 0.4 0.6 0.8 1 Lifecycle Stages - 0.4 0.6 0.8 1 ●●● 0 0.2 Lifecycle Stages 0.4 0.6 ● ● ●● ● ● 0.8 0 ● ● ● ● ● 0.2 0.4 0.6 0.8 1 - 8.5 7.0 ●●● ● ● ●● ●●● ●●●●● ● ● ● ● umunity Cross Entropy 8.0 ● ●● ● 6.5 ● ● ● - (f) Out-degree Server Fault 7.5 umunity Cross Entropy 7.0 6.8 6.6 6.4 ● ● ●● ● ● ●●●● ● ● Lifecycle Stages - (e) Out-degree SAP 18 From Mining to Understanding: The Evolution of Social Web Users ● .2 1 ● ● Lifecycle Stages (d) Out-degree Facebook umunity Cross Entropy ● ●● 3.0 3.5 4.0 4.5 5.0 5.5 6.0 ● ● ●●● ● ● ● ● ● 8.0 0.2 ● ● ● ● ● ● 7.5 0 ● ● ●● ●● ● ● ● ● Community Cross Entropy ● ● ● ● ● 3.0 3.5 4.0 4.5 5.0 5.5 6.0 2.5 ● Community Cross Entropy 3.5 3.0 ● 2.0 Community Cross Entropy 4.0 I.e.Facebook users that a user contacted differ from the Fault how do the SAP Server community? ● ● pe to at is fea ●● ● ● ● ● ● ● ● ● ●● ● of fo pr 18 ra sc us fea (ii
    • ●● ● 0.8 2.0 1.5 m(u, s + 1) m(u, s) dm = the standard lineards model: f (x; w) m(u,x. We include the = w| s) m (u, s) = 2. Build the prediction model L2 -regulariser within the model to control for overfitting on Where training splits and test di↵erent measure models. In the m is indexed by the given -indexed (i.e. in-deg •  Define the objective function using vectorabove goal is to minimise period learning the model’s weight the w, our magnitude funct cross-entropy), the minimising the a given measure (m) vector: to return the magnitude ofwith respect •  Learn the model by cost function (C(w))objective: to the weight!for use ●● ● ● ● ●● ● ● ●●● ● ● ● ● 1 0 s 0.2 0.4 0.6 0.8 1 Lifecycle Stages ●● ● Where the latter term (kwk ) defines the L2this 3. Apply the model Goal: learn theby reducinge↵ect on the w regularizer’s -regularizer and x =[m1 (u,defines .the weight of m2 (u, 2), . . . , m2 (u, 19), . . 2), . . , m1 (u, 19), •  Over ‘held-out’ data and thus controls for overfitting on the training split: model, m1 (u, 2), . . . , m1 (u, 18), m2 (u, 1), . . . , m2 (u, 18)] |w| ⇣X ⌘ •  Evaluate performance: how accurate is our 2predictor? 2 2 ● ● ● ● ●● ● ● ●●●● ● ● ● ● ● ● 19 From Mining to Understanding: The Evolution of Social Web Users ● 0.8 3.0 3.5 4.0 4.5 5.0 5.5 6.0 ● Community Cross Entropy at the allotted lifecycle period. Thus a feature vector - (c) In-degree X 1 2 (f (xi ; thesei 2 + and (6) Server Fault of the model formedC(w) a single user using w) y )rate kwk2 magnitu is for = 2|Dtrain | Error i=1 features: ● s ● ● 0.5 ● •  Change in the magnitude into from period s to s+1 ● 1.0 ● ● Comumunity Cross Entropy 2.5 feature definition and model specification, we alter the l lexical term distributions cycle period notation from the existing interval tuple set ( h signalPredicting Churners [t, t0 ] 2 T ) to use a set of discrete single elements: s 2 we see a growing ds for both churners and where S = {1, 2, . . . , 20}. Magnitude features are defin es of the curves are the as a given user’s measure taken at a given lifecycle peri 1. Extract Featuresm(u, s),Users’ Evolution cross-entropy curves at lifecy from where the measure for user u is taken •  Magnitude period s. Rates@ period s changes in measures from o of the signal are defined as lifecycle period to the next: 1 ● 0 0.2 0.4 0.6 0.8 Lifecycle Stages 1 kwk2 = j=0 |wj | 1 2 (7) As a result of using both rates and magnitudes from ea For learning the parameter weight vector (w) we use graof the 20 lifecycle periods, aside from the first and last o
    • Evaluation: Results Higher = better Area Under the receiver operator characteristic Curve (AUC) scores for the di↵erent regu Min = 0, Max = 1! on models and the J48 baseline art baseline =State of the model from the state of the art (denoted by J48 ). Best mo is in bold and significance of improvement over the random model baseline is indicated. Platform Facebook J48 = 0.586 SAP J48 = 0.759 Server Fault J48 = 0.796 Feature Set =0 =1 =2 =5 In-degree 0.535. 0.543. 0.538. 0.556* Out-degree 0.674*** 0.666*** 0.676*** 0.696*** Lexical 0.633*** 0.630*** 0639*** 0.637*** Cross-period 0.649*** 0.642*** 0.649*** 0.652*** Cross-community 0.684*** 0.693*** 0.691*** 0.699*** All 0.811*** 0.804*** 0.816*** 0.817*** In-degree 0.652*** 0.651*** 0.651*** 0.652*** Out-degree 0.741*** 0.742*** 0.742*** 0.742*** Lexical 0.501 0.501 0.501 0.499 Cross-period 0.614*** 0.614*** 0.614*** 0.613*** Cross-community 0.765*** 0.765*** 0.765*** 0.765*** All 0.816*** 0.817*** 0.817*** 0.817*** In-degree 0.659*** 0.658*** 0.662*** 0.663*** Out-degree 0.618*** 0.617*** 0.616*** 0.619*** Lexical 0.680*** 0.682*** 0.687*** 0.686*** Cross-period 0.671*** 0.675*** 0.680*** 0.691*** Cross-community 0.778*** 0.779*** 0.780*** 0.778*** All 0.858*** 0.860*** 0.861*** 0.861*** Significance codes: p-value < 0.001 *** 0.01 ** 0.05 * 0.1 . 1 = 10 0.549** 0.690*** 0.641*** 0.651*** 0.701*** 0.819*** 0.654*** 0.743*** 0.497 0.612*** 0.765*** 0.818*** 0.663*** 0.626*** 0.684*** 0.689*** 0.779*** 0.860*** ods to late lifecycle periods. Across all three platOur churn prediction approach makes use of the 20 find that performance improves as additional inment signals that users exhibit along both social an From Mining to Understanding: The Evolution of Social Web Users is added into the models. There are di↵erences, dimensions in order to di↵erentiate between who w in the gradient in performance between the platand who will remain within the online community p
    • By mining users’ evolution signals we can accurately predict who will churn, and who will not… …this enables the early application of retention strategies 21 From Mining to Understanding: The Evolution of Social Web Users
    • 22 Recommending Items from Taste Evolution From Mining to Understanding: The Evolution of Social Web Users
    • Recommender Systems aim to either: (i)  Predict item adoptions (ii)  Predict item ratings
    • duced from the training segment. There include the general number ofthe given dataset (µ), which is shown in have beenas he bias of items within a particular category that Figure 7 reviewed, we instead include the ratings withincalculating er Recommendation Datasets: Item-Ratings when the training the mean rating score across all ratings Table 1: Statistics review define used sets, the former (D u,s,c ) the distribution. use of the mean onfor our analysis and experiments - i.e. of segment. The We first datasets two its own is insu cient train Scale Dataset #Users #Items #Ratings Time Span Ratings corresponding to the in ratings scores for[26-04-2000,31-12-2000] items by during interval s for Movies MovieLensthe variance ratings3,678 u 902,585 6,024 [1,5] ng note the Amazon Movie u,s from Tweetings& therefore 889,173 also include the corresponding to category Reviews 19,043 latter 7,880,387 [20-08-1997,25-10-2012] i ) [1,10] c, and we 253,059 (Dtrain[28-02-2013,23-09-2013] the 11,451 117,206 ) item bias (b and s’ dataset Amazon Movies- TV [0,5] u,s,c u,s Total ratings by u during s,The former 8,900,178Dthe average sets are hence Dtrain ✓ is the user bias (bu ). 914,240 268,188 bias is train , these deviation User …with score r… ar formed as follows:u… for the item i within the training segfrom the mean bias n-coverage. ment, while the latter bias is thethe statistics ofdeviation frominthe 2 demonaverage these datasets shown Table u,s,c Dtrain = , t to s, c 2 (i)} strates the extent 2 which the items, users and ratings have t-4.2 Amazon Movies{(u, i, r, t) : (u, i, r, t) 2 Dtrainratingspresents the distribution of reviews mean bias from the training segment’s Figure 2 by user u. been reduced. (1) For the Amazon Movie and TV Reviews dataset we per users within each of the reduced datasets; we note that …atstrategy t MovieLens (concentrating on users time of erprovided with Amazon Standard Identification Numberswere …rated item i… (ASINs) the collection u,s looked up the ASINs for each item who µ=7.7 reviewed more than 20 skews rsas identifiers of items.train µ=3.7 {(u, i, r, t) : (u, i, r, t) have Dtrain , t 2 s} items)(2) the distribuD We the = Product Advertising 2 users who have produced many reviews, while µ=4.1 in the dataset by querying Amazon tion towards reAPI and returning the item information including: title, acfor Movie Tweetings and the Amazon datatsets we see heavy We then MovieLens directors. Unlike define the function Tweetings, tailed distributions. derive the Table also indicates irtors, andnot provided within the year and Movieinformation ave rating toboth users2 and itemsav- that there is a we were release overall, erage rating valueof from all ofratinglarge reduction innumber of ratings given great, this sugquadruples in the is not as however the from the API, therefore to perform the disambiguation sereduction in the manticset: we used the actor information from each movie: URIs gests two things: (i) mapped items are popular, and thus our intuition being that each movie would have a unique set dominate the ratings; and (iii) obscure items are present X of actors starring in it. Therefore we stored the actors associwithin the data. In particular for the Amazon dataset , deu,s information 1 spite our alignment covering only 10.6% of items we only ated within each item as additional background ) = ave2 rating(D5train 2 3 5 6have8 reduction of 126.9% of r3 total(3)5 suggesting 1 3 4 2 4 and performed disambiguation in a similar vein as1 above: 4 u,s 7 | a 9 the ratings, |Dtrain we cover the ‘headLifetime (in ratings user Average Rating Average Rating u,s the days) per distribution in terms we first identified candidate URIs for a given movie item that ’ of 100 ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●●●●● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●●●●●●● ●●●● ● ● ●● ●● ● ● ●●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ●●●● ●●●● ● ● ● ● ●●●●●●●● ● ● ● ● ● ●●● ●●●● ● ●●● ● ● ● ● ● ● ● ● ●●●●●●●●● ● ● ●●● ●● ● ● ● ●●●●●●●●●●● ●● ● ●● ●●●● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ●●●●● ●●●●●●● ●●●● ● ● ● ● ● ●●●●● ● ●●● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ●●●●●● ●●● ●●●● ● ● ●●●●●●●●●●●● ●●●● ● ●●●●●●●● ●●● ● ●● ● ●●●● ● ● ● ●● ● ●●● ● ● ●●●●●●●●●●●●●●●●●●●●●●● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●● ● ●●●●● ●●●●●●●●●● ● ●● ●● ●●●●●●●●●●●● ● ● ● ●●●●● ●●● ● ● 10−4 ● ● ● p(x) 10 ● ● ● −6 10−2 10−1 100 10 ● −8 ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●●●●●●●● ● ●● ●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ●●●●●●●● ● ● ● ● ● ● ●●●●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ●● ●● ●● ● ● ●● ● ●● ● ● ● ● ● ●●●● ●●●● ● ●● ● ● ● ● ● ●●● ●●●● ●●●●● ● ● ● ● ● ● ● ●● ●● ●● ●●●●● ●●● ●● ● ● ● ●● ●● ● ●● ● ● ●● ● ●●●●● ● ●●●●●●●● ●● ●● ● ●● ● ● ● ● ● ● ●● ● ●● ●●●● ● ●● ●●●● ● ● ● ● ●● ● ● ●●●● ●● ● ● ● ● ●● ●●●● ●●●● ●●● ● ● ●● ●● ● ● ●● ● ● ●● ● ● ● ●●●●● ● ●●●●●●●●●●●●●●● ● ● ●●● ●● ●● ● ● ● ●●● ●● ● ●●●●●● ● ● ● ● ● ●●●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●●●●●●●●●●●●●●●● ● ● ● ●● ● ● ● ●●●● ●●●● ● ● ●● ●●●●●●●● ●●●●●●●●●●● ● ● ● ● ● ●●● ●● ● ●● ●●●● ●●●●●●●●●●●●●●●●●●●●●●●● ●● ● ●● ● ●● ●●●●● ●●●●●●●●●●●●●●●●●● ● ●●● ● ●● ●● ●●● ●●●●●●●●● ●●●●●●●●● ● ●●● ● ● ●●●● ● ●●● ●●●●●●●●● ●● ●●●● ●●●●●● ● ● ●●●● ●● ●●●●●●●● ● ●●●●●●●●●●●●●●●● ●●●●●● ●●●●●●●●●●● ●● ●● ●●●●● ●●● ● ● ●●●●● ●●● ● ● ● ● ● ●● ●●●●●● ●●●●●● ●●●●●●●●●● ● ●● ● ● ● ●● ●●●●●●●●●● ● ●● ● ●● ● ●●●●●●● ●●●●●●●●●●●●●●●●●●● ● ● ● ● ● ●●●●●● ● ●●●●●●●●●●●●●●●●● ● ● ●● ●● ●●● ●●●●●●●● ●●●●● ●● ●● ●●●● ● ●●●●●●●●●● ● ●● ●●● ●● ●●●●●●●●●● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ●●●● ● ● ● ● ●●●● ●●●●●●●● ●●●●●●●●●●●●●●●●● ● ●● ●●● ●● ●●●●● ●●●●●●●●●●●●●●●●● ● ● ●● ●● ●●●●●● ●●●●●●●●●●●●●● ● ●● ● ● ●●●●●●●●●●●● ●●● ● ●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ● ● ●●● ●●● ●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●● ●●●●● ●●●●●●●● ●●●●●●●●●●●●●●●● ●●●●● ●● ●● ●● ●●●●●●●●●●●●●●●●●●●●● ● ● ● ● ●● ●● ● ●●●●●●●●●●●●●●●●●●● ● ● ●● ●●● ● ●●● ●●● ●●●●●●●●●● ● ● ● ● ● ● ● ●●●●●●● ● ●● ●● ● ● ●●● ● ● ● ●● ● ●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●● ●● ● ●●● ●● ●●●●●●●●●●●●●● ● ●● ●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ● ●●●● ●●●●●●●●●●●●●●●●●●●●●●●● ●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●● 10 10 −4 ● ● ● ●●●●●●●●●●●●●●●●● ● ● ● ●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●● ● ● ● ●●●●●●●●● ●● ●●●● ● 10 ● ● 10 ● p(x) ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ●● ●● ●● ●●● ● ● ● ●● ● ● ● ● ● ●●●●● ●● ● ●●●●● ● ● ●●●● ● ● ● ●● ● ● ● ● ●●●● ● ●●●● ●●●●● ●●●● ● ● ●● ●● ●● ●●●●●●●●● ●●● ● ●●●●●● ●●● ● ● ●●●● ● ●●● ● ● ●●● ● ●● ● ● ● ●●●●●●●●●●● ● ●● ● ● ● ●●●●●●●● ● ●● ●●●●●●●●●● ● ● ●● ●●●● ● ●●●●● ●● 10−3 p(x) ● −3 2 −4 ● ● ● −5 10−2 ● (u,i,r,t)2Dtrain by performing fuzzy matches between the item title and seof popularity. 25 titles. We then derived the correct URI by mantic URIs’ From Mining to actors associated with the Social Web comparing the set of Understanding: The Evolution of item (Aa ) Users and the set of actors associated within each candidate URI 100 ● ● ● ●● ●● µ=5.8 −4 µ=139.7 10−1 100 ● ●● ● ● ● ●● ●●● ●● ● ●●● ●● ●●● ●● ●● ● ●● ● ● ●● ● ●● 2 10−2 (a) Lens (b) Tweetings (c) Amazon From these definitions we then derive the discrete probability distribution of the user’s ratings per category as fol- µ=12.5 ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●
    • 3 2 1 Average Rating 7.0 6.0 Independent Films Directorial Debut Films 0 Directorial Debut Films 1990s Comedy Films 5.0 Average Rating 4 5 8.0 3.8 3.6 3.4 3.2 3.0 Average Rating 4.0 the biases of the recommendation models and consider the movie ‘Alien’u, v denote u then information returned. For instance, for the • restability of a given bias in in 1970, which we shall now use as a running example, denote it item leased light of when the rating is being • i, j W made: i.e. considering the the following categories are rating Forming Taste Profiles fluctuation of the found: signal • r denotes a k the and how this relates <h tpreviouspfluctuations. o u r c e / A l i e n ( f i l m )> to t p : / / d b e d i a . o r g / r e s denotes a pre d c t e r m s : s u b j e c t c a t e g o r y : A l i e n ( f r a n c h i s e ) •i lDatasets base f ms ; are port dcterms : s u b j e c t c a t e g o r y :1979 h o r r o r f i l m s ; dcterms : s u b j e c t c a t e g o r y : S p a c e a d v e n t u r e f i l mD ; s and are seg are a d c t e r m s : s u b j e c t c a t e g o r y : F i l m s s e t i n t h e f u t u r etest ) datas . (D the t such that Dt 71% Subject categories form a hierarchical structure such that0 • c, c ing c parent categories define more general subjects. For instance denote t from graph and C the category category:Films_set_in_the_future is linked twee itself to category:Science_fiction_films_by_genre by the pred- is deno May Jul Sep Nov Mar Apr May Jun Jul Aug 1998 2002 2006 2010 cove notes Time Time Time icate skos:broader, thus providing a general taxonomic clas- the set ings sification of the film. The advantage of such a structure is concept nect (a) Lens (b) Tweetings (c) Amazon and that we can explicitly identify a given user’s tastes at a given rected graph point in time via the categories of films that they have con- e 0 from i.e. deno Item sumed, and thus rated. In order to provide such information, c,c betw Rating Item Rating s the triple c s Figure 3: Average ratings require a link between a given item within and thusMov Alien however, we derived using a 7-day 4* Space_adventure (4+4)/2 = one 4 mo It of our top-2 datasets frequently rated three most and the semantic URI that denotes moving average of the Bladerunner 5* Science Fiction (4+5+4)/3 = 4.3 denotes a the that movie item. However in deriving semantic web•URIs categories. from mantic categ Star Wars films we may encounter ambiguity issues where multiple 4* for the films share the same title - this often happens with u,s,c tion of semaI film reProbability of user rating category we use available information from traintion either l the ave ) u,s makes. Therefore c P r(c|Dtrain ) = X rating(D each of0 (4) m u,s,c Mov high our datasets to disambiguate the semantic ave rating(Dgories: p : I in lifecycle period s: URIs and thus ) train 5. ANALYSING TASTE EVOLUTION this u,s Twe 26 return the correct alignment. In c0 2Ctrain section we describe connected gr view FromAnalysing the evolution and development of users’ tastes Mining to Understanding: The this disambiguation procedure across the three datasets usEvolution of Social Web Users Based on this formalisation we can3 assess the relative allows one to understand how a given rating is likely categoryyeara of theuser and lifecycle I.e. Using not ing two methods: one based on title and for given movie the sta mean user score per to rate American Films Black and White Films
    • 0.220 0.290 ● 1 2 3 4 Lifecycle Stages (a) Lens 5 ● ● 0.205 0.275 0.225 ● 0.215 0.285 ● ● ● 0.210 ● ● Conditional Entropy ● ● 0.280 Conditional Entropy 0.245 ● 0.235 Conditional Entropy rate items in the future given their category information. Conversely, for MovieLens and Movie Tweetings we see an Conditional-Entropy: relative profiles become less opposite e↵ect: users’ taste information differencepredictable I.e. how dissimilar is the user’s ratings in period s from period s-1? as they develop; users rate items in a way that renders uncertainty in profiling from previous information. 1 2 3 4 Lifecycle Stages (b) Tweetings 5 1 2 3 4 5 Lifecycle Stages (c) Amazon Figure 5: Parent category conditional entropy be27 tweento Understanding: The Evolution oflifecycle stages (e.g. H(P2 |P3 )) consecutive Social Web Users From Mining across the datasets, together with the bounds of the
    • 2 3 4 Lifecycle Stages (a) Lens 5 0.136 2 3 ● 4 Lifecycle Stages (b) Tweetings 5 0.134 ● 0.132 0.114 1 ● ● 0.130 1 ● ● Transfer Entropy 0.116 ● ● 0.112 ● Transfer Entropy 0.122 ● 0.120 Transfer Entropy 0.124 ings and Amazon we find a di↵erent e↵ect: users’ transfer entropy actually increases over time, indicating that users Transfer-Entropy: influence of globalpreferences, and therefore are less influenced by global taste behaviour on the user I.e. how does collective user behaviour influence the user’s tastes? their the ratings of other users, and instead concentrate on own tastes. 1 ● 2 3 ● 4 5 Lifecycle Stages (c) Amazon Figure 6: Parent category transfer entropy between 28 consecutive lifecycle stages (e.g. H(P2 |P3 )) across the From Mining to Understanding: The Evolution of Social Web Users datasets, together with the bounds of the 95% con-
    • nalisation + q| p + |R(u)| recommendation model as component of the 1 Model yj Formulation rui = bui ˆ 6.1 Recommendation 2 (19) u i ws: Including Taste Evolution in a Recommender System Current j2R(u) We use the following model for our recommenderWork! system X | factorisation: 1 based upon matrix pu Personalisation component: f latent factors rui = bui + qi ˆ + |R(u)| 2 yj f (19) we have three latent ufactor i: •  Predict rating for user for item vectors: qi 2 R dej2R(u) f latent factors associated with the item i; pu 2 Rf rui = bui with qi user u; and ˆ + p| the (8) u f he f we have three associated 6.2 Biases ove, latent factors latent factor vectors: qi 2 R dedenotes biases in user u and item ilatent factoritem i; p for Bias component of our model the fThe the factors associated with the asvector u 2 Rf latent f dimension are defined follows: m the set ofbias component to include taste evolution signal: es the f latent factors associated u: R(u). user la- and •  Modify rated items by user with the The u; rs fare derived duringStatic learning,latent shall vector Evolving R denotes the fz dimension zas we factorexplain for }| { }| { hile the the setui of rated iitems+ bi,cats(i)u:isR(u). pri- la- (9) j from numberof factors toucapture (f ) bu,cats(i) b = µ + b + b by user + set a The is often set to 50 across the actors are derived categories of item i literature. we shall explain during learning, as The factors How global tastes for the have 6.2.1 number of factorsitems, for instance Ro-ofpri-i Static Biases the tastes evolvedu have evolved)for categories item nifying the , while attributes acrossHow the toof user capture (f is set a The bias component inthe model containsThe biases omedies or Action Films of ourpersonalisation component: We this is often setcategoriesacrossthe movies domain. factors into 50 within the literature. static •  Interpolate duced from the training segment. Therefor each the general include sequation 19 to incorporate maths to be shown here! instance Rolatent factors for Too across the items, re unifying attributes much bias of the given dataset (µ), item from. Our in29 tegory thatUnderstanding: Thehas Films inwhich is shown in Figure 7 as rated an c Comedies a user scoreof across all ratings within the training From Mining to or Action Evolution Social Web Users the movies domain. We the mean rating hind this inclusion is that certain categories have a d Equation 19 to incorporate latent factors for each se-
    • 3 Average Rating 2 1 7.0 6.0 Independent Films Directorial Debut Films 0 Directorial Debut Films 1990s Comedy Films 5.0 Average Rating 4 5 8.0 3.8 3.6 3.4 3.2 3.0 Average Rating 4.0 di↵erent datase and then selecting the top-2 most frequent. In Figure 3 we interested in un plotted the development of the average rating score across lution di↵ers, a these two categories, derived using a 7-day moving average for the platform to smooth the variance in rating fluctuations. We find that there are peaks and troughs in the reviewing of the items 5.1 Pream that belong to the categories, in particular one can note that From this po for MovieLens the scores remain relatively stable, while for ommender syst Movie Tweetings ‘Independent Films’ reduce in their average By modelling tasteDebut films’ increase in their average evolution we can capture… ease of legibilit rating and ‘Directorial for set notation rating over time. Such information can be encoded within the biases of the recommendation models and • u, v denot (i)  the influencebias in light of dynamics consider the of global when the rating the user on is being stability of a given • i, j denot (ii)  made: the user’s preferences for of the rating signal how i.e. considering the fluctuation categories change • r denotes and how this relates to previous fluctuations. denotes a (iii)  how global tastes are evolving • Datasets D and are (Dtest ) da such that • c, c0 deno graph and itself is d May Jul Sep Nov Mar Apr May Jun Jul Aug 1998 2002 2006 2010 notes the Time Time Time nect conc (a) Lens (b) Tweetings (c) Amazon rected gra 30 i.e. ec,c0 d From Mining to Understanding: The Evolution of Social Web Users the triple Figure 3: Average ratings derived using a 7-day American Films Black and White Films
    • 31 Conclusions From Mining to Understanding: The Evolution of Social Web Users
    • p(y|y ) salient di↵erentiating feature. y2Ys , 2.5 Stat m(u, s), where the mea z }| period s. Rates= µ + bi are defi bui lifecycle period to the n 6.2.1 Static Biases ● ● ● 0.4 ● ● ●● ● ● ● ● ● ● ● ●●● 3.0 ● ● ●●● ● ● ● ●● ●● ● ● 1.5 m ● ● ●● ● 1.0 ● ● ● ● ●● ● ● ●●● ● ● ● ● 0.5 0.6 ●● 4.0 ● ● ● 3.5 0.8 dm User evolution can be captured using lifecycle models component The bias (u, s) = ds duced from the trainin bias of the given datas Where m is indexed the mean rating score period cross-entropy), u segment. The use of th to return the magnitud note allotted lifecycle at thethe variance in ra dataset therefore we is formed- for a single u the user features: bias (bu ). The from the mean bias fo x =[m1 (u, the . . , m ment, while 2), . latter1 ( mean bias from. . . , tra m1 (u, 2), the m1 ● ● ●● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● µ=3.7 ● ● ● As a result of using b of the 20 lifecycle perio for magnitudes and the provided with at most 18 magnitude features 1 2 3 4 5 rate featuresRating each for Average scribe within the exper (a) Lens used between di↵erent: features, community cro (ii) lifecycle periods. On Figure 7: Distribut the research questions t three datasets into a user’s lifecycle c constraining the feature eratively increasing the 6.2.2 Category Bia ● ● ● ● ● 1 ● ●● ● 2 3 ●● ●● ● ● ● ● ● ● ● 4 Lifecycle Stages 0.2 0.4 0.6 0.8 ● 5 ● ● ● ● ●● ● ● 1 2 ●●● ●● ● 3 ● ● ●● ●● ● ● 4 Lifecycle Stages 0.2 0.4 0.6 0.8 0 1 0 32 Lifecycle Stages Lifecycle Stages (a) Lens From Mining to Understanding: The Evolution of Social (b) Tweetings Web Users 5 1 Comumunity Cross Entropy Transfer Entropy ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● 1 0 ● 2 3 4 Lifecycle Stages 0.2 0.4 0.6 0.8 5 1 (c) Amazon (g) Lexical - Face- (h) Lexical - SAP (i) Lexical - Server book Fault Lifecycle Stages ● ● ● 10−4 7.0 0.1307.5 0.132 8.0 0.134 8.5 0.136 ● Transfer Entropy Comumunity Cross Entropy 6.0 0.112 6.5 7.0 0.114 7.5 8.0 0.116 Users’ tastes are susceptible to global taste influence ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ●●● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ●● ●● ●● ●●● ● ● ● ●● ● ● ● ● ● ●●●●● ●● ● ●●●●● ● ● ●●●● ● ● ● ●● ● ● ● ● ●●●● ● ●●●● ●●●●● ●●●● ● ● ●● ●● ●● ●●●●● ●●●● ● ● ● ●●●●●●●●● ●● ● ●●●●● ●●● ● ● ●●● ● ●● ● ● ● ●●●●●●●●●●● ● ●● ● ● ● ●●●●●●●● ● ●● ●●●●●●●●●● ● ● ●● ●●●● ● ●●●●● ●● ● ● ●●●●●●●●●●●●●●●●● ● ● ● ●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●● ● ● ●●●●●●●●● ●●● ● ● ● ●●●●●●●●●●● ●●●●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●●●●●●●● ● ●● ●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●● ● p(x) ●●● ● ● ●●●● 10−2 ● ● ● ● ● ● p(x) ● ● ● 10−3 ● ● Community Cross Entropy ● 3.0 3.5 4.0 4.5 5.0 5.5 6.0 ● ●● ●● ● ● ● ● Community Cross Entropy 3.5 3.0 2.5 ● ● 2.0 Community Cross Entropy ● ● 3.0 3.5 4.0 4.5 5.0 5.5 6.0 Churners and non-churners exhibit divergent signals Transfer Entropy Comumunity Cross Entropy 0.120 6.4 6.6 6.8 7.0 0.124 0.122 6.0 6.2 3.  2.0 ● 4.5 We derived the transfer entropy between consecutive lifecycle periods, as with the conditional entropy above, to examine how the influence of global and local dynamics on users’ taste profiles developed over time. Figure 6 plots the means 0.2 1 0.6 0.8values across3the lifecycle periods n 0.8 1 of 0.4 these 1 2 0 0.2 0.4 0.6 0.8 1 … 0 0.2 0.4 together 0 0.6 Lifecycle Stages Lifecycle Stages Lifecycle users of with the 95% confidence intervals. We find that Stages MovieLens transfer (b) In-degree over In-degree (a) In-degree - entropy decrease - (c) time, indicating that global dynamics have a stronger Server Fault users’ influence on Facebook SAP taste profiles towards later lifecycle stages. Such an e↵ect is characteristic of users becoming more involved and familiar with the review system, and as a consequence paying attention to more information from the users. With Movie Tweetings and Amazon we find a di↵erent e↵ect: users’ transfer entropy actually increases over time, indicating that users are less influenced by global taste preferences, and therefore 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 the ratings of other users, and instead concentrate Stages their Lifecycle Stages Lifecycle Stages Lifecycle on own tastes. (d) Out-degree - (e) Out-degree - (f) Out-degree Facebook SAP Server Fault 4.0 2.  Comumunity Cross Entropy 5.0 1.0 Comumunity Cross Entropy Churners Non−churners ● 0.2 1.  Comumunity Cross Entropy 1.2 y 0 2Ys 1 , x2Xs 1
    • 33 Questions? @mrowebot m.rowe@lancaster.ac.uk http://www.lancaster.ac.uk/staff/rowem/ Changing with Time: Modelling and Detecting User Lifecycle Periods in Online Community Platforms. M Rowe. International Conference on Social Informatics. Kyoto, Japan (2013) Mining User Lifecycles from Online Community Platforms and their Application to Churn Prediction. Understanding: The Evolution of Social Web Users From Mining to M Rowe. International Conference on Data Mining. Dallas, US. (2013)