Now showing 1 - 2 of 2
  • Publication
    Mining the Real-Time Web: A Novel Approach to Product Recommendation
    Real-time web (RTW) services such as Twitter allow users to express their opinions and interests, often expressed in the form of short text messages providing abbreviated and highly personalized commentary in real-time. Although this RTW data is far from the structured data (movie ratings, product features, etc.) that is familiar to recommender systems research, it can contain useful consumer reviews on products, services and brands. This paper describes how Twitter-like short-form messages can be leveraged as a source of indexing and retrieval information for product recommendation. In particular, we describe how users and products can be represented from the terms used in their associated reviews. An evaluation performed on four different product datasets from the Blippr service shows the potential of this type of recommendation knowledge, and the experiments show that our proposed approach outperforms a more traditional collaborative-filtering based approach.
      6237Scopus© Citations 80
  • Publication
    Towards tagging and categorization for micro-blogs
    Abstract. Micro-blogging services are becoming very popular among users who want to share local or global news, their knowledge or their opinions on the real-time web. Lately, users are also using these services to search for information, and some services include tag or category information to better facilitate search. However, these tags are typically free-form in nature with users permitted to adopt their own conventions without restriction, which can make the set of tags noisy and sparse. A solution to this problem is to recommend tags (or categories) to users. Our work represents an initial study in the recommendation of categories for short-form messages in order to provide for better search and message filtering. In particular, we describe how such real-time web data can be used as a source of indexing and retrieval information for category recommendation. An evaluation performed on two different micro-blogging datasets indicates that promising performance is achieved by our approach.
      2194