4 research outputs found
eBay users form stable groups of common interest
Market segmentation of an online auction site is studied by analyzing the
users' bidding behavior. The distribution of user activity is investigated and
a network of bidders connected by common interest in individual articles is
constructed. The network's cluster structure corresponds to the main user
groups according to common interest, exhibiting hierarchy and overlap. Key
feature of the analysis is its independence of any similarity measure between
the articles offered on eBay, as such a measure would only introduce bias in
the analysis. Results are compared to null models based on random networks and
clusters are validated and interpreted using the taxonomic classifications of
eBay categories. We find clear-cut and coherent interest profiles for the
bidders in each cluster. The interest profiles of bidder groups are compared to
the classification of articles actually bought by these users during the time
span 6-9 months after the initial grouping. The interest profiles discovered
remain stable, indicating typical interest profiles in society. Our results
show how network theory can be applied successfully to problems of market
segmentation and sociological milieu studies with sparse, high dimensional
data.Comment: Major revision of the manuscript. Methodological improvements and
inclusion of analysis of temporal development of user interests. 19 pages, 12
figures, 5 table
Empirical analysis of web-based user-object bipartite networks
Understanding the structure and evolution of web-based user-object networks
is a significant task since they play a crucial role in e-commerce nowadays.
This Letter reports the empirical analysis on two large-scale web sites,
audioscrobbler.com and del.icio.us, where users are connected with music groups
and bookmarks, respectively. The degree distributions and degree-degree
correlations for both users and objects are reported. We propose a new index,
named collaborative clustering coefficient, to quantify the clustering behavior
based on the collaborative selection. Accordingly, the clustering properties
and clustering-degree correlations are investigated. We report some novel
phenomena well characterizing the selection mechanism of web users and outline
the relevance of these phenomena to the information recommendation problem.Comment: 6 pages, 7 figures and 1 tabl
Information filtering via self-consistent refinement
Recommender systems are significant to help people deal with the world of information explosion and overload. In this letter, we develop a general framework named self-consistent refinement and implement it by embedding two representative recommendation algorithms: similarity-based and spectrum-based methods. Numerical simulations on a benchmark data set demonstrate that the present method converges fast and can provide quite better performance than the standard methods