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--- volunteer_data:volunteer_matching [2024/03/30 05:03] – [Weekly Progress] tangyuanbo
+++ volunteer_data:volunteer_matching [2024/04/13 09:07] (current) – [Weekly Progress] gankx
@@ Line 48: / Line 48: @@
 === Meeting: March 30, 2024 ===
+  * {{:volunteer_data:3.30report.pptx}}
+  *   * === **Attendees:** ===
+  * professor Yang Li, Yuanbo Tang, Yang Chen, Kaixuan Gan (16:00-17:00)
+  * === **Discussion** ===
+  * Yuanbo presented a report of his work in the last week.H1: The topic analysis of existing textual data.
+  * Yang Chen shared the potential acquisition of more valuable data in the future.
+  * Discuss the plan of future data processing and topic analysis
+  * === **Future plan** ===
+  * Literature Review on personnel assignment problem and user categorization/profiling for recommendation
+  * Further acquisition of data.
+  * Topic analysis of further data.
+=== Meeting: April 6, 2024 ===
+  *   * === **Attendees:** ===
+  * professor Yang Li, Yuanbo Tang, Kaixuan Gan (16:30-17:30)
+  * === **Discussion** ===
+  * Kaixuan presented a report of his work in the last week.H1: Using autoencoder for dimensionality reduction and clustering.
+  * professor Yang Li gave some advice on analysis of clustering results.
+  * Discuss the plan of future data processing.
+  * === **Future plan** ===
+  * Literature Review on personnel assignment problem and user categorization/profiling for recommendation
+  * Quantitative analysis of clustering results.
+  * Performing one-hot encoding on categorical data.
+=== Meeting: April 13, 2024 ===
+  * {{ :volunteer_data:4.13report.pptx |}}
+  *   * === **Attendees:** ===
+  * professor Yang Li, Yuanbo Tang, Dongdong Zhang, Yang Chen，Qitai Tan， Kaixuan Gan (16:00-17:00)
+  * === **Discussion** ===
+  * Kaixuan presented a report of his work in the last week.H1: Using autoencoder for dimensionality reduction and clustering.
+  * professor Yang Li gave some advice on data processing.
+  * Discuss the plan of future acquisition of data.
+  * === **Future plan** ===
+  * Literature Review on recommendation system.
+  * Perform clustering analysis again after adding more data.
+  * Further acquisition of data.