Online social network based object recommendation system

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Online social network based object recommendation system

  1. 1. Sriram Patil (201305532) Nishit Soni (201002026) Jiten Goyal (201101040) Information Retrieval and Extraction (CSE474) International Institute ofTechnology Hyderabad Online Social Network Based Object Recommendation System
  2. 2. Index Concept & Architecture Challenges Problems with usual techniques Our approach Recommendation relevance criteria Future Scope
  3. 3. Concept & Architecture Social network based object recommendation Recommending movies Social Network: Facebook Dataset: International Movie Database (IMDB) Website Login with Facebook and fetch users liked movies Server Spark Web Framework Jetty Web Server MySQL IMDB Dataset
  4. 4. Challenges Two different sources (Facebook and IMDB) Sparsity Even active users may have liked well under 5% percent of the movies. Scalability Billions of users and Millions of movies. Duplication As movie ids are different. Have to match the movies with names.
  5. 5. Problems with usual techniques Nearest Neighbour algorithms require computation that grows with both the number of users and the number of movies. With billions of users and millions of movies, a typical web based recommender system running existing algorithms will suffer serious scalability problems. Because of sparsity, a recommender system based on nearest neighbour algorithm may be unable to make any movie recommendation for a particular user. As, a result the accuracy of the recommendations may be poor. Ever growing data and users set.
  6. 6. Our approach Fetch friends with whom the user has atleast some common movies. If no common movies, then select all the friends. Get movies liked by the friends. For each movie, we get a recommendation score. Parameters considered while assigning score: Friends which share some likes with the user Friends of same gender Friends of same age group Movies with same genre Sort the score and return top “n” movies.
  7. 7. Recommendation relevance criteria As the movies are recommended from a lot of friends, it is little tricky to figure out if the recommendations are relevant. We used two criteria Movies suggested by Facebook. Our results are comparable to Facebook movie suggestions. And even better in some cases. It is a good recommendation if the user has already watched that movie.
  8. 8. Enhancements There are some more parameters which can be considered while ranking a movie Friend list like “Close Friends”, “Relatives”, etc can be given a little extra weight. Actors and directors of the movies can be considered when ranking. Similar recommendation systems can be extended to recommend music, books, etc.

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