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CS297 Proposal

Recipe Suggestion Tool

Sakuntala P Gangaraju (

Advisor: Dr. Chris Pollett


Many a times, we prefer a search for recipes based on ingredients. Current search engines do not provide this feature. There is a strong need for domain specific search engine based on recipes. Many recipe search engines are available (like,, As per my analysis these search engines do not provide / need further improvement in the following areas:

  • Recipe search by ingredients.
  • Better clustering of data returned by search queries.
  • Provide search filters (like searching within the recipes submitted in the last X number of days).
  • Provide links to view images of ingredients of a recipe.
  • Provide links to grocery stores carrying the items listed in the recipe based on the location of the user.
  • Suggest recipes to users based on their previous searches.
  • Provide option to modify the quantities of ingredients based on servings selected by users.

My project aims to combine the features available in different search engines under one search engine and provide an intuitive interface including the new features listed above.

Most of the recipe search results in current websites are not efficiently clustered based on relevance / categories resulting in a user getting lost in the huge search results presented.

I plan to explore different hierarchical clustering algorithms and distance measures to find an efficient algorithm that can be used to cluster recipe data matching user’s queries. This will help categorize recipe results and also present user with a % of relevance to the original query making it easier for user to select recipes.

As part of this project I also plan to build custom search engine wrappers around existing search engines to help search images of ingredients and provide local listings of grocery stores carrying ingredients listed in a recipe.


Week 1-2: Jan. 24-Feb 6Study Dr.Pollett's Search Engine Code.
Week 3: Feb. 7- 13Deliverable 1 due: Results of conducted study.
Week 4-5: Feb. 14- 27Search and study research papers on clustering algorithms.
Week 6: Feb. 28- Mar 6.Deliverable 2 due: Report on implementation of K-means clustering algorithm.
Week 7-8: Mar. 7- 20.Pseudo code implementation of clustering based on minimum spanning tree.
Week 9: Mar. 21- 27.Deliverable 3 due: Report on the implementation of clustering based on minimum spanning tree.
Week 10-11: Mar. 28- Apr 10.Pseudo code implementation of the custom search engine wrappers for providing image search.
Week 12: Apr. 11- 17.Report on Wrapper implementation of the image based custom search engine.
Week 13-14: Apr. 18- May 1.Merging all the deliverables and producing a presentable one.
Week 15: May. 2- 8.Work on Deliverable 5: CS297 Report.
Week 16: May. 9- 15Deliverable 5 due: CS297 Report.


The full project will be done when CS298 is completed. The following will be done by the end of CS297:

1. Report of Study on Dr.Pollett's search engine code.

2. Report on implementation of K-means clustering algorithm.

3. Report on the implementation of clustering based on minimum spanning tree.

4. Report on Wrapper implementation.

5. CS297 Report.


[Kwok97] Kwok, S.H & Constantinides, A. G.(1997).A Fast Recursive Shortest Spanning Tree for Image Segmentation and Edge Detection. IEEE Transactions on Image Processing, 6(2).

[Jain99] Jain, A.K., Murty, M.N., Flynn, P.J.(1999). Data clustering: a review. ACM Computing Surveys, 31(3), 264 - 323.

[Google] Google Custom Search. Retrieved from

[Yahoo] Yahoo! Search BOSS. Retrieved from