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Learning collaborative filtering and its applications to people to people recommendation in social networks
conference contribution
posted on 2023-05-23, 09:18 authored by Cai, X, Bain, M, Krzywicki, A, Wobcke, W, Kim, YS, Compton, P, Mahidadia, APredicting people who other people may like has recently become an important task in many online social networks. Traditional collaborative filtering (CF) approaches are popular in recommender systems to effectively predict user preferences for items. One major problem in CF is computing similarity between users or items. Traditional CF methods often use heuristic methods to combine the ratings given to an item by similar users, which may not reflect the characteristics of the active user and can give unsatisfactory performance. In contrast to heuristic approaches we have developed CollabNet, a novel algorithm that uses gradient descent to learn the relative contributions of similar users or items to the ranking of recommendations produced by a recommender system, using weights to represent the contributions of similar users for each active user. We have applied CollabNet to the challenging problem of people to people recommendation in social networks, where people have a dual role as both “users” and “items”, e.g., both initiating and receiving communications, to recommend other users to a given user, based on user similarity in terms of both taste (whom they like) and attractiveness (who likes them). Evaluation of CollabNet recommendations on datasets from a commercial online social network shows improved performance over standard CF.
History
Publication title
Proceedings of the 10th IEEE International Conference on Data MiningEditors
GI Webb, B Liu, C Zhang, D Gunopulos, X WuPagination
743-748ISBN
978-1-4244-9131-5Department/School
School of Information and Communication TechnologyPublisher
Institute of Electrical and Electronics EngineersPlace of publication
United States of AmericaEvent title
10th IEEE International Conference on Data MiningEvent Venue
Sydney, AustraliaDate of Event (Start Date)
2010-12-14Date of Event (End Date)
2010-12-17Rights statement
Copyright 2010 IEEERepository Status
- Restricted