Code Review and Acceralation

ReadMe

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+Update the Report and PPT

Report/references.bib

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+@book{ricci2011introduction,  
+  title={Recommender Systems Handbook},  
+  author={Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B},  
+  year={2011},  
+  publisher={Springer US}  
+}  
+
+@inproceedings{sarwar2001item,  
+  title={Item-based collaborative filtering recommendation algorithms},  
+  author={Sarwar, Badrul and Karypis, George and Konstan, Joseph and Riedl, John},  
+  booktitle={Proceedings of the 10th international conference on World Wide Web},  
+  pages={285--295},  
+  year={2001}  
+}  
+
+@article{koren2009matrix,  
+  title={Matrix factorization techniques for recommender systems},  
+  author={Koren, Yehuda and Bell, Robert and Volinsky, Chris},  
+  journal={Computer},  
+  volume={42},  
+  number={8},  
+  pages={30--37},  
+  year={2009}  
+}  
+
+@inproceedings{he2020lightgcn,  
+  title={LightGCN: Simplifying and powering graph convolution network for recommendation},  
+  author={He, Xiangnan and Deng, Kuan and Wang, Xiang and Li, Yan and Zhang, Yongdong and Wang, Meng},  
+  booktitle={Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},  
+  pages={639--648},  
+  year={2020}  
+}  
+
+@inproceedings{wang2019neural,  
+  title={Neural graph collaborative filtering},  
+  author={Wang, Xiang and He, Xiangnan and Wang, Meng and Feng, Fuli and Chua, Tat-Seng},  
+  booktitle={Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval},  
+  pages={165--174},  
+  year={2019}  
+}  
+
+@misc{movielens,  
+  author = {Harper, F. Maxwell and Konstan, Joseph A.},  
+  title = {The MovieLens Datasets: History and Context},  
+  journal = {ACM Transactions on Interactive Intelligent Systems},  
+  volume = {5},  
+  number = {4},  
+  pages = {19:1--19:19},  
+  year = {2015},  
+  publisher = {ACM},  
+  url = {https://grouplens.org/datasets/movielens/}  
+}  
+
+@inproceedings{lastfm,  
+  author={Cantador, Iván and Brusilovsky, Peter and Kuflik, Tsvi},  
+  title={Second Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011)},  
+  booktitle={Proceedings of the 5th ACM Conference on Recommender Systems},  
+  year={2011},  
+  publisher={ACM},  
+  url={https://grouplens.org/datasets/hetrec-2011/}  
+}  
+
+@misc{yelp,  
+  author={{Yelp}},  
+  title={{Yelp Dataset Challenge}},  
+  url={https://www.yelp.com/dataset},  
+  year={2023}  
+}  
+
+@article{adomavicius2005toward,  
+  title={Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions},  
+  author={Adomavicius, Gediminas and Tuzhilin, Alexander},  
+  journal={IEEE transactions on knowledge and data engineering},  
+  volume={17},  
+  number={6},  
+  pages={734--749},  
+  year={2005},  
+  publisher={IEEE}  
+}  
+
+@article{pazzani2007content,  
+  title={Content-based recommendation systems},  
+  author={Pazzani, Michael J and Billsus, Daniel},  
+  journal={The adaptive web},  
+  pages={325--341},  
+  year={2007},  
+  publisher={Springer}  
+}  
+
+@article{su2009survey,  
+  title={A survey of collaborative filtering techniques},  
+  author={Su, Xiaoyuan and Khoshgoftaar, Taghi M},  
+  journal={Advances in artificial intelligence},  
+  volume={2009},  
+  year={2009},  
+  publisher={Hindawi}  
+}  
+
+@article{bell2007lessons,  
+  title={The bellkor solution to the netflix prize},  
+  author={Bell, Robert M and Koren, Yehuda},  
+  journal={KorBell Team's Report to Netflix},  
+  year={2007}  
+}  
+
+@article{wu2022graph,  
+  title={Graph neural networks in recommender systems: A survey},  
+  author={Wu, Shiwen and Sun, Fei and Zhang, Wentao and Cui, Bin},  
+  journal={ACM Computing Surveys},  
+  volume={55},  
+  number={5},  
+  pages={1--37},  
+  year={2022},  
+  publisher={ACM New York, NY}  
+}  
+

data/ml-1m/README

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+SUMMARY
+================================================================================
+
+These files contain 1,000,209 anonymous ratings of approximately 3,900 movies 
+made by 6,040 MovieLens users who joined MovieLens in 2000.
+
+USAGE LICENSE
+================================================================================
+
+Neither the University of Minnesota nor any of the researchers
+involved can guarantee the correctness of the data, its suitability
+for any particular purpose, or the validity of results based on the
+use of the data set.  The data set may be used for any research
+purposes under the following conditions:
+
+     * The user may not state or imply any endorsement from the
+       University of Minnesota or the GroupLens Research Group.
+
+     * The user must acknowledge the use of the data set in
+       publications resulting from the use of the data set
+       (see below for citation information).
+
+     * The user may not redistribute the data without separate
+       permission.
+
+     * The user may not use this information for any commercial or
+       revenue-bearing purposes without first obtaining permission
+       from a faculty member of the GroupLens Research Project at the
+       University of Minnesota.
+
+If you have any further questions or comments, please contact GroupLens
+<grouplens-info@cs.umn.edu>. 
+
+CITATION
+================================================================================
+
+To acknowledge use of the dataset in publications, please cite the following
+paper:
+
+F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History
+and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4,
+Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872
+
+
+ACKNOWLEDGEMENTS
+================================================================================
+
+Thanks to Shyong Lam and Jon Herlocker for cleaning up and generating the data
+set.
+
+FURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT
+================================================================================
+
+The GroupLens Research Project is a research group in the Department of 
+Computer Science and Engineering at the University of Minnesota. Members of 
+the GroupLens Research Project are involved in many research projects related 
+to the fields of information filtering, collaborative filtering, and 
+recommender systems. The project is lead by professors John Riedl and Joseph 
+Konstan. The project began to explore automated collaborative filtering in 
+1992, but is most well known for its world wide trial of an automated 
+collaborative filtering system for Usenet news in 1996. Since then the project 
+has expanded its scope to research overall information filtering solutions, 
+integrating in content-based methods as well as improving current collaborative 
+filtering technology.
+
+Further information on the GroupLens Research project, including research 
+publications, can be found at the following web site:
+
+        http://www.grouplens.org/
+
+GroupLens Research currently operates a movie recommender based on 
+collaborative filtering:
+
+        http://www.movielens.org/
+
+RATINGS FILE DESCRIPTION
+================================================================================
+
+All ratings are contained in the file "ratings.dat" and are in the
+following format:
+
+UserID::MovieID::Rating::Timestamp
+
+- UserIDs range between 1 and 6040 
+- MovieIDs range between 1 and 3952
+- Ratings are made on a 5-star scale (whole-star ratings only)
+- Timestamp is represented in seconds since the epoch as returned by time(2)
+- Each user has at least 20 ratings
+
+USERS FILE DESCRIPTION
+================================================================================
+
+User information is in the file "users.dat" and is in the following
+format:
+
+UserID::Gender::Age::Occupation::Zip-code
+
+All demographic information is provided voluntarily by the users and is
+not checked for accuracy.  Only users who have provided some demographic
+information are included in this data set.
+
+- Gender is denoted by a "M" for male and "F" for female
+- Age is chosen from the following ranges:
+
+	*  1:  "Under 18"
+	* 18:  "18-24"
+	* 25:  "25-34"
+	* 35:  "35-44"
+	* 45:  "45-49"
+	* 50:  "50-55"
+	* 56:  "56+"
+
+- Occupation is chosen from the following choices:
+
+	*  0:  "other" or not specified
+	*  1:  "academic/educator"
+	*  2:  "artist"
+	*  3:  "clerical/admin"
+	*  4:  "college/grad student"
+	*  5:  "customer service"
+	*  6:  "doctor/health care"
+	*  7:  "executive/managerial"
+	*  8:  "farmer"
+	*  9:  "homemaker"
+	* 10:  "K-12 student"
+	* 11:  "lawyer"
+	* 12:  "programmer"
+	* 13:  "retired"
+	* 14:  "sales/marketing"
+	* 15:  "scientist"
+	* 16:  "self-employed"
+	* 17:  "technician/engineer"
+	* 18:  "tradesman/craftsman"
+	* 19:  "unemployed"
+	* 20:  "writer"
+
+MOVIES FILE DESCRIPTION
+================================================================================
+
+Movie information is in the file "movies.dat" and is in the following
+format:
+
+MovieID::Title::Genres
+
+- Titles are identical to titles provided by the IMDB (including
+year of release)
+- Genres are pipe-separated and are selected from the following genres:
+
+	* Action
+	* Adventure
+	* Animation
+	* Children's
+	* Comedy
+	* Crime
+	* Documentary
+	* Drama
+	* Fantasy
+	* Film-Noir
+	* Horror
+	* Musical
+	* Mystery
+	* Romance
+	* Sci-Fi
+	* Thriller
+	* War
+	* Western
+
+- Some MovieIDs do not correspond to a movie due to accidental duplicate
+entries and/or test entries
+- Movies are mostly entered by hand, so errors and inconsistencies may exist