1 research outputs found
Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests
Collaborative Filtering is largely applied to personalize item recommendation
but its performance is affected by the sparsity of rating data. In order to
address this issue, recent systems have been developed to improve
recommendation by extracting latent factors from the rating matrices, or by
exploiting trust relations established among users in social networks. In this
work, we are interested in evaluating whether other sources of preference
information than ratings and social ties can be used to improve recommendation
performance. Specifically, we aim at testing whether the integration of
frequently co-occurring interests in information search logs can improve
recommendation performance in User-to-User Collaborative Filtering (U2UCF). For
this purpose, we propose the Extended Category-based Collaborative Filtering
(ECCF) recommender, which enriches category-based user profiles derived from
the analysis of rating behavior with data categories that are frequently
searched together by people in search sessions. We test our model using a big
rating dataset and a log of a largely used search engine to extract the
co-occurrence of interests. The experiments show that ECCF outperforms U2UCF
and category-based collaborative recommendation in accuracy, MRR, diversity of
recommendations and user coverage. Moreover, it outperforms the SVD++ Matrix
Factorization algorithm in accuracy and diversity of recommendation lists