# Identifying Human Daily Activity Types with Time-Aware Interactions
## 5 Discussion
### The Relationship between Activity Types and Related Terms  
While the proposed method trains the samples, various vectors, including post, word, and user vectors, are obtained, corresponding to the posts, words in posts, and users.  
To examine the relationships between activity types and related text words, we considered the word vectors as a sample and employed semantic similarity evaluation to conduct experiments. We first manually select key words from the activity type name as the center word for each activity, respectively. The dataset contains 14 activity types, which are \"Food\", \"Beauty&spa\", \"Entertainment\", \"Travel\", \"Shopping\", \"Service\", \"Sports\", \"Health\", \"Car\", \"Nightlife\", \"Pets\", \"Education\", \"Religious\" and \"Mass media\". The selected 14 center words are \"Food\", \"Beauty\", \"Entertainment\", \"Travel\", \"Shopping\", \"Service\", \"Sports\", \"Health\", \"Car\", \"Nightlife\", \"Pets\", \"Education\", \"Religious\", and \"Mass media\". Then, we calculated the cosine distances between vectors of all the words to the vector of 14 center words and listed the top 7 closest words for each center word in Table 9.  
As shown in Table 9, the top 7 closest words in each row usually appear in the same activity scene, as words in the same activity type can be correctly mapped into the same vector space. It suggests that the training process have successfully captured the semantic relationship between activity types and text words and the identified activity type is meaningful.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Precision** & **F1** \\ \hline Mic (TG-HAGCN) & **0.9259** & **0.9259** \\ Mic (TG-HAGCN/Date) & 0.9216 & 0.9216 \\ Mic (TG-HAGCN/User) & 0.9226 & 0.9226 \\ Mic (TG-HAGCN/Date \&/User) & 0.9218 \\ \hline Mac (TG-HAGCN) & **0.8877** & **0.7774** \\ Mac (TG-HAGCN/Date) & 0.8443 & 0.7593 \\ Mac (TG-HAGCN/User) & 0.8558 & 0.7356 \\ Mac (TG-HAGCN/Date \&/User) & 0.8775 & 0.7739 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Precision and F1 score of ablation study. “TG-HAGCN /Date” means that the date nodes are removed in the TG-HAGCN model and the input graph is only “user-post-word” graph. “TG-HAGCN /User” means to remove user nodes from TG-HAGCN model, that is, the input graphs are “post-word” graph and “date-word” graph. “TG-HAGCN/Date \& /User” means that the input graph is only “post-word” graph.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Center** & & & & & & \\
**Word** & & & & & & & \\ \hline Food & drink & foods & supplies & meat & fresh & eating & fish \\ Beauty & psyche & beautiful & beast & truth & her & pleasure & happiness \\ Entertainment & theater & movie & stage & museum & acrobatics & film & dancer \\ travel & travels & trip & journey & explore & go & visit & visitors \\ shopping & mall & restaurant & retail & shops & entertainment & stores & attractions \\ Service & clean & customer & carpet & realtor & professional & fix & technician \\ Sports & sport & teams & football & sporting & racing & clubs & basketball \\ Health & medical & care & mental & health & treatment & disease & benefits \\ Car & vehicle & oil & auto & repair & kia & drive & mechanic \\ Nightlife & bar & drink & night & club & dj & beers & pub \\ Pets & dogs & cat & cats & mouse & horses & mice & boss \\ Education & school & teacher & class & student & college & learning & instructor \\ Religious & political & religion & social & christian & spiritual & secular & moral \\ Mass media & radio & news & music & listen & channel & listener & tv \\ \hline \hline \end{tabular}
\end{table}
Table 9: Words that have the closest semantics with the selected words (the semantic distance is increased from left to right).