An iterative semi-explicit rating method for building collaborative recommender systems
Introduction
Recommender systems have gained more importance ever before as the increasing popularity of Internet and social networking, e.g., electronic commerce, Web 2.0, and web personalization. Over the last decade, they are ones of the most successful applications both in academia and in industry. Success stories can be found in recommending books and CDs at Amazon.com (Linden, Smith, & York, 2003), movies by MovieLens (Miller, Albert, Lam, Konstan, & Riedl, 2003), news by GroupLens (Konstan et al., 1997) and by MONERs (Lee & Park, 2007), ESL reading lessons (Hsu, 2008) and so forth. Nonetheless, current state-of-the-art shows that they require further improvements to make them more effective and applicable to a broader range of real-life applications. For example, developments of better methods for representing user behavior and the information about the items to be recommended, more advanced recommendation methods that incorporate various contextual information into the recommendation process and utilize multi-criteria ratings, and less intrusive and more flexible recommendation methods require to be further enhanced (Adomavicius & Tuzhilin, 2005). The paper particularly concentrates on an improvement of capturing better user behaviors, i.e., rating the user preference.
Rating for recommender systems (or collaborative filtering in particular) results in a user-item preference matrix by means of either explicit rating or implicit rating. In the explicit rating, each user examines items and assigns them rating values on a rating scale, while in the implicit rating the rating values are presumed based on the user’s behaviors such as purchase of the item, access to the information content, time duration to read the content, actions (e.g., save, print, delete) applied to the content, etc. It is reported that the explicit rating captures user preferences to items more accurately than implicit rating does (Nichols, 1998). However, the latent problem of the explicit rating, i.e., data sparsity (which is usually severer than that of the implicit rating), makes it hard to manipulate the rating matrix – i.e., recommending items to an active user – in a pragmatic sense.
The paper aims to propose a novel rating method, namely semi-explicit rating (SER), to overcome the sparsity problem. The proposed method extrapolates the rating scores of unrated elements in the principle of semi-supervised learning (Jeong et al., 2008, Lee and Lee, 2005, Lee and Lee, 2006, Lee and Lee, 2007), in that by manipulating a few labeled/rated elements mathematically a number of the rest unlabeled/unrated elements are estimated. Especially to enhance the recommendation accuracy, the proposed method iteratively updates the user-item preference matrix until it becomes stabilized.
The remainder of the paper is organized as follows: Section 2 addresses previous works on recommender systems, especially on collaborative filtering. Section 3 presents the details of the proposed method, followed by preliminary validations via numerical experiments in Section 4. Finally, the concluding remarks and future works are given in Section 5.
Section snippets
Related works
Due to massive diversity in algorithms and applications, this section briefly reviews the key research branches of the recommender systems and collaborative filtering relevant to this paper. For more comprehensive reviews and comparison, see references such as Adomavicius and Tuzhilin (2005), Deshpande and Karypis (2004) and Candillier, Meyer, and Boullé (2007).
The recommendation problem is to maximize an active user’s satisfaction by suggesting him/her a set of items from many. According to
Semi-explicit rating and recommendation prediction
This section presents a novel extrapolation method, namely semi-explicit rating (SER), that estimates unrated elements in the user-item preference matrix. The method is based on the semi-supervised learning principle, in that a number of unrated elements are filled by numerical inference of a few (sparse) explicit ratings.
Simulation setting
Preliminary simulations are conducted to validate the underpinning concept of the proposed method. The simulation is limited for it is intended only to show the validity of using the method. The dataset used is the MovieLens (ML) data, which contain 100,000 explicit ratings (on 1–5 rating scale) from 943 users and 1682 items (Sarwar et al., 2001). Note that the ML data are very sparse: the sparsity level is about 93.7% (i.e., ). For the underlying
Conclusion
The recommender systems, or collaborative filtering in particular, have been omnipresent in various applications such as products recommendation, spams filtering, web personalization, etc. As the amount of information content grows, the importance of accurate recommender systems increases. The availability of correct user-item preference matrices is critical to build a better system. The explicit rating method usually gives a better preference matrix than the implicit rating methods does.
Acknowledgements
Thanks to Shyong Lam and Jon Herlocker for cleaning up and generating the MovieLens (ML) data set, and to Cai-Nicolas Ziegler and Ron Hornbaker for the Book-Crossing (BX) data set. This work was supported partially by the Korea Research Foundation under the Grant No. KRF-2008-314-D00483 and partially by the KOSEF under the Grant No. R01-2007-000-20792-0.
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