tailieunhanh - Data Analysis Machine Learning and Applications Episode 3 Part 4

Tham khảo tài liệu 'data analysis machine learning and applications episode 3 part 4', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 620 Panagiotis Symeonidis In this paper we construct a feature profile of a user to reveal the duality between users and features. For instance in a movie recommender system a user prefers a movie for various reasons such as the actors the director or the genre of the movie. All these features affect differently the choice of each user. Then we apply Latent Semantic Indexing Model LSI to reveal the dominant features of a user. Finally we provide recommendations according to this dimensionally-reduced feature profile. Our experiments with a real-life data set show the superiority of our approach over existing CF CB and hybrid approaches. The rest of this paper is organized as follows Section 2 summarizes the related work. The proposed approach is described in Section 3. Experimental results are given in Section 4. Finally Section 5 concludes this paper. 2 Related work In 1994 the GroupLens system implemented a CF algorithm based on common users preferences. Nowadays this algorithm is known as user-based CF. In 2001 another CF algorithm was proposed. It is based on the items similarities for a neighborhood generation. This algorithm is denoted as item-based CF. The Content-Based filtering approach has been studied extensively in the Information Retrieval IR community. Recently Schult and Spiliopoulou 2006 proposed the Theme-Monitor algorithm for finding emerging and persistent SthemesT in document collections. Moreover in IR area Furnas et al. 1988 proposed LSI to detect the latent semantic relationship between terms and documents. Sarwar et al. 2000 applied dimensionality reduction for the user-based CF approach. There have been several attempts to combine CB with CF. The Fab System Bal-abanovic et al. 1997 measures similarity between users after first computing a content profile for each user. This process reverses the CinemaScreen System Salter et al. 2006 which runs CB on the results of CF. Melville et al. 2002 used a contentbased predictor to enhance existing .