Paper Info
Title
Time-evolution of IPTV recommender systems
Abstract
In this paper we evaluate the performance of different collaborative filtering algorithms over time, where new users, new items, and new ratings are constantly added to the recommender dataset. The analysis has been performed on the datasets collected by two IPTV providers. Both datasets have been implicitly collected by analyzing the pay-per-view movies purchased by the users over a period of several months. The first result of the paper outlines that item-based algorithms perform better with respect to SVD-based ones in the early stage of the cold-start problem. The second result shows that the accuracy of SVD-based algorithms, when using few latent factors, decreases with the time-evolution of the dataset. On the contrary, SVD-based algorithms, when used with a large-enough number of latent features, increase their accuracy with time and may outperform the item-based algorithms if the dataset does not present a long-tail behavior.
Year
DOI
Venue
2010
10.1145/1809777.1809801
Proceedings of the 8th international interactive conference on Interactive TV&Video
Keywords
DocType
Citations 
svd-based algorithm,item-based algorithm,cold-start,iptv provider,new user,collaborative filtering,time evolution,latent feature,cold-start problem,recommender systems,new rating,iptv recommender system,new item,recommender dataset,latent factor,recommender system,long tail,cold start
Conference
11
PageRank 
References 
Authors
0.64
19
2
Name
Order
Citations
PageRank
Paolo Cremonesi1130687.23
Roberto Turrin285934.94