1. Team: Sleep_Deprived Sagun Pai and Sheikh Muhammad Sarwar Advisor: Dmitry Ignatov

2. We Love Collaborative Filtering There are users, places (items) and ratings!!! Place 1Place 2Place 3Place 4 user14354 user253-- user34334 user421-- user542--

3. Too Sparse!! There are 212 users and 4108 places… Only 1.3% is filled with data… For this specific problem we have cold start items…. Place 1Place 2Place 3Spb 1 user1435- user253-- user3433- user421-- user542-- We have no rating data for locations in Saint Petersburg. But we will have to recommend places…

4. Interest Expansion Approach User A tags a place P Food User B tags P as Food and Seafood Tag Union: P = {Food, Seafood} Now assume, both the users rated P high In the normal tag space user A has liking for only Food as he/she likes P In the expanded tag space he/she has also liking for Seafood as he/she has rated P higher Now for further finer granularity this seafood tag can be expanded!!

5. Further Expansion of “Seafood” tag by onelook.com

6. With Regular Interest Zone USER ID: 1234567 Interest: Restaurants,Parks,Museums,Bar-hopping,Food,Local Food,Culturally Diverse Food USER ID: 1234568 Interest: Restaurants,Museums,Bar-hopping,Food,Local Food,Culturally Diverse Food,Cafés USER ID: 1234569 Interest: Restaurants,Parks,Museums,Bar-hopping,Food,Local Food,Culturally Diverse Food

7. With Specialized Interest Zone USER ID: 1234567 Interest: Running,Mountains,Promenade,Vineyards,Mountaineering,Dolphin Watching,Fish and Chips USER ID: 1234568 Interest: Indoor Skiing,Fish and Chips,Street Musicians,Vegetarian Cuisine,Tapas,Chinatown,Comics USER ID :1234569 Interest: Running,Tropical,Mountains,Vineyards,Mountaineering,Sailing,Diving

8. Steps to Perform the Task Tag each place in the user profile using all possible tags found for it For each user find a subset of places he or she ranked for which ranking value is >=3 Find tag frequency of each tag that appears on the user profile Create interest zone vector for a user using the tags of the places he or she liked Aggregate all the components and expanded components into a single vector for a user Select top-k components of the vector and expand each component using similar words or phrases Create vector representation of all the documents in the web crawled data of Saint Petersburg Compute similarity between the user interest vector and crawled document vector using cosine similarity

9. Finally, the results! "age": "50", "gender": "male" Mikhailovsky Opera and Ballet Theater Wood Bar State Academical Mariinskiy Theatre Catherine Palace and Park Severyanin The 8th Line Pub Wave Burgers & More Pyshechnaya On Zhelyabova 25 O'Hooligans Xander Bar Images of top-3 places

10. Finally, the results! "age": "47", "gender": "male“ Meatarea Chuck Namaste Zoom Pyshechnaya On Zhelyabova 25 Casa Latina Brugge Belgian Gastronomic Pub Wave Burgers & More Zoom Cafe Dom 7 Jazz Bar Severyanin Images of top-3 places

11. Finally, the results! "age": "30", "gender": "female“ St. Isaac's Cathedral State Museum-Memorial Mikhailovsky Opera and Ballet Theater Percorso Severyanin Lampshade Meatarea Chuck Duo Gastrobar Xander Bar The 8th Line Pub State Hermitage Museum and Winter Palace Images of top-3 places

12. Thanks