• TDA Literature Review

Meeting: Jan 17, 2022

• 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.

• 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:
• shutong_experiments.pptx
• graph_attention_network.docx

Meeting: April 14, 2022

• 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:
• 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:
• tim_progress_4.21pptx.pptx

Meeting: May 16, 2022

• Attendees:
• Professor Yang Li, Shutong and Tim (14:00-15:00)
• Discussion
• Shutong presented an updated version of her research findings, challenges and proposal. Greedy algorithm solves some of the common challenges in community detection.
• Shutong discussed latest findings on NCE and community detection for Futian and Nanshan districts
• Tim showed comparative results for DGRec and GR4URec. DGRec still outperforms baselines such as UserKNN
• Comments and Future work
• Shutong to determine the relationship between SHS and NCE results
• Shutong to summarize charts and send them to Pioneers in order to get feedback on changes that correspond to the detected time slots
• Shutong to make minor recommended adjustments to the presentation such as illustrating the important next steps as a pipeline instead of explaining them in text.
• Tim to analyze data to determine the optimal time window that gives dynamic sequences
• Tim to conduct experiment at district level, for instance using Futian and/or Nanshan data

Meeting: May 23, 2022

• Attendees:
• Professor Yang Li, Shutong and Tim (14:00-15:00)
• Discussion
• Tim presented statistical information to determine appropriate time window
• Based on standard deviation and entropy, Futian and Nanshan had highest metrics at k=7 and k= 28, respectively.
• Shutong discussed the relationship between NCE, SHS and S-NCE
• Shutong reported that C-NCE has positive correlation with the number of tasks and volunteers, meaning that when the number of tasks/volunteers increase, the system becomes unstable. C-NCE also is positively correlated to the number of SHS [for more details, see attachment]

• Comments and Future work
• Shutong encountered an efficiency problem where its taking longer to compute results for Futian
• Shutong to show her computational method to pave way for alternative solutions
• Tim to consider double checking the data since the number of unique organisers keeps decreasing/constant as time window increases.
• Tim to conduct experiment at district level with induced structural breaks in the data

* Attachments shutong_cnce.pptx