Alleviating cold-user start problem with users' social network data in recommendation systems
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Alleviating cold-user start problem with users' social network data in recommendation systems

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This work explores the possibility of using relevant data from users’ ...

This work explores the possibility of using relevant data from users’
social network to alleviate the cold-user problems in a recommender
system domain. The proposed solution extracts the most valuable
node in the graph generated by check in a venue with an Android
application using the Foursquare API. By obtaining the recommendations to this node we estimate the probability of some categories
to be similar to users tastes...

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Alleviating cold-user start problem with users' social network data in recommendation systems Alleviating cold-user start problem with users' social network data in recommendation systems Presentation Transcript

  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions Questions Alleviating cold-user start problem with users’ social network data in recommendation systems Eduardo Castillejo Aitor Almeida Diego L´pez-de-Ipi˜a o n DeustoTech - Deusto Institute of Technology, University of Deusto http://www.morelab.deusto.es Preference Learning: Problems and Applications in Artificial Intelligence, 2012
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsIndex 1 Recommendation Systems How do they work What are they 2 Main problems of RS Known problems 3 Proposed solution Foursquare Eigenvector centrality Example and analysis 4 Results and evaluation Evaluation Results 5 Conclusions Conclusions Future work 6 Questions
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsHow do they work Amazon
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsHow do they work Youtube
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsWhat are they Commonly built under a web-based platform they gather information about every entity which takes part in an e-commerce interaction process to make recommendations to the users increasing the benefits of the e-commerce company. They use algorithms which base their recommendations in explicit and implicit data from the users (ratings, purchases, previous searches...).
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsKnown problems Recommendation systems are a good tool to suggest items to users based in their own interaction with the system, but they also have some intrinsic problems which are difficult to solve: Sparsity: it occurs when available data are insufficient for identifying similar users (neighbors) and it is a major issue that limits the quality of recommendations and the applicability of collaborative filtering (CF). Scalability: CF requires computations that are very expensive and grow polynomially with the number of users and items in a database. Therefore, in order to bring recommendation algorithms effectively on the web, and succeed in providing recommendations with high accuracy and acceptable performance, sophisticated data structures and advanced, scalable architectures are required.
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsKnown problems Recommendation systems are a good tool to suggest items to users based in their own interaction with the system, but they also have some intrinsic problems which are difficult to solve: Sparsity: it occurs when available data are insufficient for identifying similar users (neighbors) and it is a major issue that limits the quality of recommendations and the applicability of collaborative filtering (CF). Scalability: CF requires computations that are very expensive and grow polynomially with the number of users and items in a database. Therefore, in order to bring recommendation algorithms effectively on the web, and succeed in providing recommendations with high accuracy and acceptable performance, sophisticated data structures and advanced, scalable architectures are required. Cold-start
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsKnown problems The cold-start problem arises when a new entity enters the system for the first time. In this situation the recommendation engine can not predict suggestions because of the lack of information about the current entity. It usually includes 3 entities: Items Users Systems
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsKnown problems Amazon Recommendations related with Kindle, watches special prices and laptops... ¿?
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsKnown problems Youtube Recommendations about videos of people we don’t actually know... ¿?
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsKnown problems We focus our research in alleviating the so called cold-user problem by collecting information about the user digging in their social network interactions.
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsKnown problems We focus our research in alleviating the so called cold-user problem by collecting information about the user digging in their social network interactions. But... how?
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsFoursquare Foursquare is a location-based social networking website which allows users to ”check in” at venues using their smartphones. Thanks to its API developers can request some user data (e.g. location, friends, last check-ins, etc.). Developed Android app.
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsFoursquare Using the herenow API endpoint we get the previous check-ins done by other users at the current checked in venue. user 1 Legend user 2 Friend user Unknown user 1 hour interval 2 hours interval 3 hours interval Users check-ins time stamp current user 1:00 pm current user user 1 2:00 pm user 3 user 2 3:00 pm user 3 1:00 pm user 4 user 4 4:00 pm
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsEigenvector centrality Once we have completed the matrix, we apply eigenvector centrality. Since degree centrality gives a simple count of the number of ties a node has, eigenvector centrality acknowledges that not all connections are equal. Denoting the centrality of a node i by xi , then it is possible to make xi proportional to the average of the centralities of i’s network neighbours: where λ is a constant. This equation can be also rewritten defining the vector of centralities x = (x1 , x2 , ...):
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsExample and analysis Applying eigenvector centrality to our previous matrix A” we obtain that the eigenvalue λ = 12,502, and its eigenvector:
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsExample and analysis The first value of the vector e1 is related to the node 0 (the current user), so its value has to be ignored (in this case the highest value corresponds with the current user). The second highest value corresponds with the node 1. That means that his recommendations would be more ”pleasing” to the user.
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsEvaluation Amazon default recommendations VS. our Amazon categories estimation: We presented to our test users the default recommendations that Amazon.com for new users, and another list with our Amazon categories recommendations computed with our solution. Once our users compared both lists, they fulfilled a questionnaire to capture their satisfaction level with the obtained results. Amazon default recommendations: Kindle related products, clothing trends, products being seen by other customers, best watches prices, laptops best prices, top seller books. Amazon default categories: Home, garden and tools, clothing, shoes and jewelry; books; electronics and computers; automotive and industrials; movies, music and games; grocery, health and beauty; toys, kids and baby; sports and outdoors.
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsResults
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsConclusions This paper explores the possibility of using relevant data from users’ social network to alleviate the cold-user problems in a recommender system domain. The proposed solution extracts the most valuable node in the graph generated by check in a venue with an Android application using the Foursquare API. By obtaining the recommendations to this node we estimate the probability of some categories to be similar to users tastes...
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsConclusions This paper explores the possibility of using relevant data from users’ social network to alleviate the cold-user problems in a recommender system domain. The proposed solution extracts the most valuable node in the graph generated by check in a venue with an Android application using the Foursquare API. By obtaining the recommendations to this node we estimate the probability of some categories to be similar to users tastes... ... but we suffered several limitations: Few users and data... Near venues are not checked in enough...
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsConclusions This paper explores the possibility of using relevant data from users’ social network to alleviate the cold-user problems in a recommender system domain. The proposed solution extracts the most valuable node in the graph generated by check in a venue with an Android application using the Foursquare API. By obtaining the recommendations to this node we estimate the probability of some categories to be similar to users tastes... ... but we suffered several limitations: Few users and data... Near venues are not checked in enough... Sparsity.
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions QuestionsFuture work Include data not only from Foursquare Combine different social network analysis metrics Take into account more than the most valuable node for doing recommendations. Store the obtained matrices for each venue and update them with every check-in. Test the solution among a higher number of users.
  • Recommendation Systems Main problems of RS Proposed solution Results and evaluation Conclusions Questions Thank you again! eduardo.castillejo@deusto.es Preference Learning: Problems and Applications in Artificial Intelligence, 2012