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--- volunteer_data:top [2022/05/11 09:03] – yang
+++ volunteer_data:top [2024/03/17 22:56] (current) – yang
@@ Line 1: / Line 1: @@
 ====== Volunteer Data Analysis ======
+====Volunteer Matching====
-====Recommendation and social network analysis====
+  * [[volunteer_data:volunteer_matching| Project Page]]
+====Persistent community detection====
-**Meeting: Jan 17, 2022**
+  * [[volunteer_data:tda_review|TDA Literature Review]]
-  * Presented results of experiments on Volunteer data and Movielens
-  * Discussed the metrics for measuring performance on the two datasets
-  * Showed graphs of two methods and their performance on the two datasets
-  * Key: the BOA method still outperforms LDA in both datasets.
+====Recommendation and social network analysis====
-  * **Comments and Future work**
-  * To compute metrics as a whole and not aggregate them separately for each user.
-  * To apply BOA method on a recommendation paper and compare the results
-  * To compile a representative code and send to the professor
-  * To read literature on latest work in RS.
-**Meeting: Jan 24, 2022**
-  * Discussed the possible alternatives of embedding the data, social net embedding
-  * Refined the life document to include user history pertaining to time, location and participation history
-  * Discussed a few papers that have used GNN for prediction
-  * **Comments and Future work**
-  * To read the papers related to Social Recommendation eg, Song et al (2019)
-  * Understand the nature of embeddings they used for the input data
-  * Summarise findings of papers (using GNN) whose data is similar to ours
-**Meeting: Feb 18, 2022**
-  * Presented a Survey on GNN Social recommendation
-  * Presented Item-to-Item KNN method used by Amazon
-  * Discussed two papers on Social Recommendation and presented their findings
-  * Paper 1: GNN for Social Recommendation - Fan, Ma,  Li, He,Zhao,Tang, and Yin. 2019. (ACM, WWW)
-  * Paper 2: Session-based Social Recommendation via Dynamic Graph Attention Networks, Song, Wang and Xiao (2019) – (ACM, WSDM)
-  * **Comments and Future work**
-  * To begin working on experiments related to dynamic session-based recommendation
-  * To make a list of the features and embeddings that can be used from our data,
-  * Determine session and neighbours based on our data, eg location based or participation based.
-**Meeting: March 1, 2022**
-  * **Attendees:**
-  * Professor Yang Li, Shutong and Tim (19:00-20:00)
-  * **Discussion**
-  * Tim presented a proposal on GNN
-  * **Comments and Future work**
-  * To clarify the research problem by formulating the underlying mathematical nitty gritties
-  * To consider geographic location and tasks types as input features and determine how to incorporate them
-**Meeting: March 23, 2022**
-  * **Attendees:**
-  * Professor Yang Li, Shutong and Tim (11:00-12:00)
-  * **Discussion**
-  * Shutong presented and discussed the plan on how the experiments will be conducted.
-  * Tim presented the findings on the first experiment for Session based GNN
-  * **Comments and Future work**
-  * Tim to write a brief methodology discussing his implementation of the algorithm
-  * Need for providing details for experimental plan and hyper-parameter tuning
-**Meeting: March 31, 2022**
-  * **Attendees:**
-  * Professor Yang Li, Shutong and Tim (11:00-12:00)
-  * **Discussion**
-  * Tim presented a write up of methodology section that also included experiment plan
-  * **Comments and Future work**
-  * Tim to give more clarity on the network architecture diagram
-  * To consider node embeddings as one hot key vectors
-  * To also include task type information on the volunteer preference
-  * Tim to remind Shutong and the professor every week before each meeting.
-  * Shutong suggested Tim to use PPT to draw flowchart and diagrams
-  * Shutong suggested to provide reference code for matching task types
-**Meeting: April 7, 2022**
-  * **Attendees:**
-  * Professor Yang Li, Shutong and Tim (11:00-12:00)
-  * **Discussion**
-  * Shutong presented her preliminary experimental results
-  * Tim discussed the DGRec framework.
-  * **Comments and Future work**
-  *Shutong to continue with her experimental plan
-  *Shutong to consider narrowing the research towards a more specific problem
-  * Tim to conduct experiment and reproduce the results in the paper
-  * **Attachments:**
-  * {{:volunteer_data:shutong_experiments.pptx}}
-  * {{:volunteer_data:graph_attention_network.docx}}
-**Meeting: April 14, 2022**
+  * [[volunteer_data:meetings| Meeting Notes]]
-  * **Attendees:**
-  * Professor Yang Li, Shutong and Tim (11:00-12:00)
-  * **Discussion**
-  * Shutong presented statistical data for Pioneers to be used in an article
-  * Tim presented the reproduced results from the paper
-  * **Comments and Future work**
-  *Shutong to write a summary of her latest findings
-  * Tim to consider a naive baseline approach that will be compared against the current method
-  * **Attachments:**
-  * {{ :volunteer_data:tim_progress_4.14.pptx |}}
-**Meeting: April 21, 2022**
-  * **Attendees:**
-  * Professor Yang Li, Shutong and Tim (11:00-12:00)
-  * **Discussion**
-  * Shutong presented a summary of her proposal and latest findings
-  * Tim compared the performance of voting process/UserKNN against the model of interest
-  * **Comments and Future work**
-  * Tim to explore at least one or two graph models for further comparison
-  * Shutong to make recommended adjustments to the presentation
-  * **Attachments:**
-  * {{ :volunteer_data:tim_progress_4.21pptx.pptx |}}