Collaborative filtering by analyzing dynamic user interests modeled by taxonomy

  • Authors:

  • Makoto Nakatsuji;Yasuhiro Fujiwara;Toshio Uchiyama;Hiroyuki Toda

  • Affiliations:

  • NTT Service Evolution Laboratories, NTT Corporation, Japan;NTT Software Innovation Center, NTT Corporation, Yokosuka-Shi, Kanagawa, Japan;NTT Service Evolution Laboratories, NTT Corporation, Japan;NTT Service Evolution Laboratories, NTT Corporation, Japan

  • Venue:
  • ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

Tracking user interests over time is important for making accurate recommendations. However, the widely-used time-decay-based approach worsens the sparsity problem because it deemphasizes old item transactions. We introduce two ideas to solve the sparsity problem. First, we divide the users' transactions into epochs i.e. time periods, and identify epochs that are dominated by interests similar to the current interests of the active user. Thus, it can eliminate dissimilar transactions while making use of similar transactions that exist in prior epochs. Second, we use a taxonomy of items to model user item transactions in each epoch. This well captures the interests of users in each epoch even if there are few transactions. It suits the situations in which the items transacted by users dynamically change over time; the semantics behind classes do not change so often while individual items often appear and disappear. Fortunately, many taxonomies are now available on the web because of the spread of the Linked Open Data vision. We can now use those to understand dynamic user interests semantically. We evaluate our method using a dataset, a music listening history, extracted from users' tweets and one containing a restaurant visit history gathered from a gourmet guide site. The results show that our method predicts user interests much more accurately than the previous time-decay-based method.