*3.1.2 User-query centered knowledgebase integration*

User query analysis is complete by extracting knowledge from query log data, shown in **Figures 3** and **4**. Here, the scenario is that successive user tweets are removed based on the query log data, and the matched query is accessed

#### **Figure 4.**

*Extracting the knowledge from query log data.*

#### **Figure 5.**

*User-query centered knowledgebase integration.*

*Knowledge Extraction from Open Data Repository DOI: http://dx.doi.org/10.5772/intechopen.100234*

correspondingly through subsequent extraction. The accessed query content related to a Twitter account focuses on extracting the respective accounts retrieved from the tweets account. The latter tweet's accounts focus on the generic tweets with similar content, with identical query phrases. But during relevance query knowledge, similar user query search with less effective and provide the presence of user tweets with high similarity with different text types.

The scenario of user-query knowledgebase construction is showing in **Figure 5**. Here, from the previous methods, with the selected user-query procedure used as a tweet query category, user integration history is proposed, with knowledge extraction at every query feedback history loop-back. For query integration, the projected tweets from one day to many days are structured for continuous knowledge construction through the constant tweet's attribution and trace the future knowledge analysis, which evolves in the future based on the tweeted user query.

The model of the proposed recommendation system is shown in **Figure 6**, consists of:

a.a set TU of N users, TU = {TU1, TU2, TU3,...., TUN}

b.a set C of M items, C = {c1, c2, c3,....., cM}

c. a query cluster matrix QC, QC = [qcmn] where m ϵ TU and n ϵ C

d.a set ƒ of N feature query sets, ƒ = [ƒmn]

e. a tweet knowledge weights Kω = {ω1, ω2, … ωN}

User item set is associated with the number of feature vectors representing the tweet customers with different tweet phrases assigned to the user-query content model. In the recommender content model, the decision ranking prediction compares the users and Twitter queries in the categorized user-query item set and tweet's weights.

#### **Figure 6.**

*The proposed user-query centered recommender system.*
