Web Mining and Social Networking: Techniques and ... - tud.ttu.ee

8.3 User Profiling for Web Recommendation Based on PLSA and LDA Model 181Algorithm 8.1: User profiling algorithm for Web recommendation based on PLSAInput: An active user session s a and a set of user profiles up = {up j }.Output: The top-N recommendations REC PLSA (s a )={p mat ∣j ∣p matj ∈ P, j = 1,...,N }.Step 1: The active session s a and the discovered user profiles up are viewed as n-dimensional vectors over the page space within a site, i.e. up j =[w j 1 ,wj 2 ,···,wj n],where w ji is the significant weight contributed by page p i in the up user profile, similarlys a =[w a 1 ,wa 2 ,···wa n ], where wa i = 1, if page p i is already accessed, and otherwisew a i = 0,Step 2: Measure the similarities between the active session and all derived usageprofiles, and choose the maximum one out of the calculated similarities as the mostmatched pattern:sim(s a ,up j )=(s a · up j ) / ∥ ∥‖s a ‖ ∥upj∥2 2 (8.11)where s a up j = ∑ n i=1 w ji wa i , ‖s a‖ 2 = √ ∑ n i=1 (wa i )2 , ∥ ∥ √upj∥2 = ∑ n i=1 (w ji )2 .sim(s a ,up mat )=maxj(sim(s a ,up j )) (8.12)Step 3: Incorporate the selected profile up mat with the active session s a , then calculatethe recommendation score rs(p i ) for each page p i :√rs(p i )= w mati × sim(s a ,up mat ) (8.13)Thus, each page in the profile will be assigned a recommendation score between 0and 1. Note that the recommendation score will be 0 if the page is already visited inthe current session,Step 4: Sort the calculated recommendation scores in step 3 obtained in a descendingorder, i.e. rs =(w mat1,w mat2,···,w matn ) , and select the N pages with the highestrecommendation score to construct the top-N recommendation set:REC PLSA (s a )={p matj|rs(p matj ) > rs(p matj+1 ), j = 1,2,···N − 1} (8.14)8.3.2 Recommendation Algorithm Based on LDA ModelIn this section, we present a user profiling algorithm for Web recommendation basedon LDA generative model. As introduced in Sect.6.2, LDA is one of the generativemodels, which is to reveal the latent semantic correlation among the co-occurred activitiesvia a generative procedure. Similar to the Web recommendation algorithmproposed in previous section, we, first, discover the usage pattern by examining theposterior probability estimates derived via the LDA model, then, measure the similaritybetween the active user session and the usage patterns to select the most matched