Clustering-based Location Recommendation(Collaborative Filtering)
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The document presents a clustering-based location recommendation system developed by students under the supervision of Dr. Mohammad Rezwanul Huq, focusing on addressing the overload of information users face when seeking places to visit. It details the methodology of data cleaning, feature engineering, and clustering to assess user preferences while utilizing the Foursquare NYC check-in dataset. The proposal concludes with evaluation techniques and future work considerations aimed at enhancing the recommendation system's performance.
Clustering-based Location Recommendation(Collaborative Filtering)
- 1. Presented By Md. Farhan Tanvir(2014-2-60-124) Kevin Stephen Bishwas (2014-2-60-091) Nazmul Hasan(2014-2-60-063) Supervised By Dr. Mohammad Rezwanul Huq Assistant Professor Department Of Computer Science And Engineering East West University . Clustering-based Location Recommendation System 1
- 2. The world is an over-crowded place 2
- 3. They all want to get our attention 3
- 4. We are overloaded • Thousands of news places to visit • Millions of restaurants , hotels , parks to visit . 4
- 5. 5
- 6. Can Google Help ? • Yes, but only when we really know what we are looking for • What if I just want some interesting place to visit? – Btw, what does it mean by “interesting”? 6
- 7. Can Facebook Help ? • Yes, I tend to find my friends’ stuffs interesting • What if I had only few friends, and what places they visit do not always attract me? 7
- 8. Can experts help? • Yes, but it won’t scale well – Everyone receives exactly the same advice! • It is what they like, not me! – Like restaurant , what get expert approval does not guarantee attention of the mass . 8
- 9. OK, Here is the idea called Recommendation System • Recommendation system is an information filtering technique, which provides users with information, which user may be interested in . • Based on - Past Behavior - Relations to the user - Item Similarity - Context 9
- 10. Existing Work • Ling Li*, Ya Zhou, Han Xiong, Cailin Hu, Collaborative filtering based on user attributes and user ratings for restaurant recommendation , 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) . • Zhiyang Jia , Wei Gao , Yuting Yang , Xu Chen , User-based Collaborative Filtering for Tourist Attraction Recommendations , 2015 IEEE International Conference on Computational Intelligence & Communication Technology. • Lakshmi Tharun Ponnam (Author) , Sreenivasa Deepak Punyasamudram ,Siva Nagaraju Nallagulla , Srikanth Yellamati , Movie Recommender System Using Item Based Collaborative Filtering Technique , 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS) . 10
- 11. Our Proposal Input Dataset Data Cleaning Feature Engineering Clustering Find User Preference Result 11
- 12. Our Dataset • Foursquare NYC Check-in Dataset • https://sites.google.com/site/yangdingqi/home/foursquare-dataset 12
- 13. Attributes of our Dataset 13 1 User ID 2 Venue ID 3 Venue Category ID 4 Venue Category 5 Latitude 6 Longitude 7 Time zone offset 8 UTC time But after Data cleaning and feature engineering we’ve got some other attribute . What Data Cleaning and Feature Engineering ?
- 14. Task 1: Data Cleaning • Removing Home Check-Ins: -The dataset did not contain the home check-ins for all the users . After cleaning with certain process we removed this. 14
- 15. Task 1: Data Cleaning(Cont…) • Replacing Multiple category of a venue: User Id Venue Id Venue Category Id Venue Category 1 V-1 C001 Bar 1 V-1 C002 Bar 1 V-1 C001 Bar 1 V-1 C002 Bar 1 V-1 C002 Park Figure : Before Replacing User Id Venue Id Venue Category Id Venue Category 1 V-1 C002 Bar 1 V-1 C002 Bar 1 V-1 C002 Bar 1 V-1 C002 Bar 1 V-1 C002 Bar Figure : After Replacing 15
- 16. Task 1: Data Cleaning(Cont…) • Replacing Sub-Category Id’s From Category Id Column: User Id Venue Id Venue Category Id Venue Category 1 V-1 C001 Bar 1 V-2 C002 Bar 1 V-3 C001 Bar 1 V-4 C002 Bar 1 V-5 C002 Bar Figure : Before Replacing User Id Venue Id Venue Category Id Venue Category 1 V-1 C002 Bar 1 V-2 C002 Bar 1 V-3 C002 Bar 1 V-4 C002 Bar 1 V-5 C002 Bar Figure : After Replacing 16
- 17. Task 1: Data Cleaning(Cont…) • Replacing different latitude and longitude value of a venue: Figure : Before Replacing Figure : After Replacing Venue Id Latitude Longitude V-1 40 -73 V-1 43 -70 V-1 43 -70 V-1 40 -73 V-1 40 -73 17 Venue Id Latitude Longitude V-1 40 -73 V-1 40 -73 V-1 40 -73 V-1 40 -73 V-1 40 -73
- 18. Task 2: Feature Engineering • Check-In Counts: User Id Venue Id Check-In Count 1083 V-1 3 1083 V-2 1 1083 V-3 1 1083 V-4 2 1083 V-5 1 Figure : After adding Check-In Count attribute 18
- 19. Task 2: Feature Engineering(Cont…) • Venue Distance from User’s Center: - First We find out users center point by doing average of latitude and longitude where user has previously checked . -Now, Using this center points we calculate the distance of each ven using “The Haversine Formula”. Where, • d is the distance between the two points, • r is the radius of the sphere, • φ1, φ2: latitude of point 1 and latitude of point 2, in radians • λ1, λ2: longitude of point 1 and longitude of point 2, in radians Reference : https://www.movable-type.co.uk/scripts/latlong.html 19 𝒅 = 𝟐𝒓 𝐬𝐢𝐧−𝟏 𝐬𝐢𝐧 𝟐 𝝋 𝟐 − 𝝋 𝟏 𝟐 + 𝐜𝐨𝐬 𝝋 𝟏 𝐜𝐨𝐬 𝝋 𝟐 𝐬𝐢𝐧 𝟐 𝝀 𝟐 − 𝝀 𝟏 𝟐
- 20. Our Dataset After Feature Engineering 20 1 User ID 2 Venue ID 3 Venue Category ID 4 Venue Category 5 Latitude 6 Longitude 7 Distance From Center 8 Check In Count
- 21. Task 2 : Clustering • We used KNN (k-nearest neighbors) as clustering algorithm . • First we find the similarity between user using Pearson correlation . We also checked cosine correlation but Pearson Correlation gives us better result . Where:- Rui, Rvi represent the checkingCount of ith item given by the user u and v respectively. Ru , Rv represent the average checkin of user u and v respectively. Iuv donates the set of items checked by both user u and v 𝒔𝒊𝒎 𝒖, 𝒗 = 𝒊∈𝑰 𝒖𝒗 𝑹 𝒖𝒊 − 𝑹 𝒖 . 𝑹 𝒗𝒊 − 𝑹 𝒗 𝒊∈𝑰 𝒖𝒗 𝑹 𝒖𝒊 − 𝑹 𝒖 𝟐 𝒊∈𝑰 𝒖𝒗 𝑹 𝒗𝒊 − 𝑹 𝒗 𝟐 21 Reference : Collaborative filtering based on user attributes and user ratings for restaurant recommendation
- 22. Task 2 : Clustering(Cont…) • After finding similarity we take top n nearest neighbor . • Then used their checkinCount to find predicted checkinCount for every places of that user which user didn’t check in . We used weighted average checkin to predict checkin count for a user . • After this we took top most checkInCount. 22
- 23. Task 3 : Find User Preference • We used user’s every check-in’s distance from center point and find a mean distance. If user’s most of the checkin’s distance are more than mean distance we can say user like to travel in long distance otherwise like to travel in close distance . Then we sort the recommendation on user preference . • Example : Users mean checkin distance = 50 KM User’s have 50 checkins . 30 of them are more than 50 km. Result : Users Love o travel in long distance 23
- 24. Example Place1 Place2 Place3 Place4 Me 3 - 5 ? My Friend 4 6 - - You 3 - 5 6 Another guy 4 2 - 1 Your Friend 8 - - 3 What will be probable checking count of Place4? 24
- 25. Example(Cont..) Place1 Place2 Place3 Place4 Me 3 - 5 ? My Friend 4 6 - - You 3 - 5 6 Another guy 4 2 - 1 Your Friend 8 - - 3 25
- 26. Example(Cont..) Place1 Place2 Place3 Place4 Me 3 - 5 6 My Friend 4 6 - - You 3 - 5 6 Another guy 4 2 - 1 Your Friend 8 - - 3 26
- 27. Evolution • We used Sampling and RMSE technique for evaluating our recommendation. • In sampling technique 10% of the entire dataset was selected randomly without replacement to make a sample dataset. • RMSE technique was used to evaluate the algorithm. It calculated the error of a predicted check in count from an actual check in count of a venue by specific user in test dataset. RMSE Formula: RMSE = 𝑖=1 𝑁 𝑃 𝑢,𝑖−𝑅 𝑢,𝑖 2 𝑁 Here : P u,i=is the predicted checkIn Count for user u on venue i R u,i=is the actual checkIn Count for user u on venue i N=is the total number of venues where user checked in Reference : Collaborative filtering based on user attributes and user ratings for restaurant recommendation 27
- 28. RMSE Graph 28 Figure : RMSE graph
- 29. Demo • We have created a simple demo where user can enter their id and our system will recommend place for user . Figure : Input User Id Figure : Output Recommendation 29
- 30. Future Work 30 • Try Model Based Recommendation System • Add More Domain • Try Triangulation Technique to find user’s center point .
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