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A Random-Walk Based Scoring Algorithm with Application to Recommender Systems for Large-Scale E-Commerce
| Content Provider | Semantic Scholar |
|---|---|
| Author | Gori, Marco Pucci, Augusto |
| Copyright Year | 2006 |
| Abstract | Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present ”ItemRank”, a random–walk based scoring algorithm, which can be used to rank products according to expected user preferences, in order to recommend top– rank items to potentially interested users. We tested our algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies, and we compared ItemRank with other state-of-the-art ranking techniques (in particular the algorithms described in [1, 2]). Our experiments show that ItemRank performs better than the other algorithms we compared to and, at the same time, it is less complex than other proposed algorithms with respect to memory usage and computational cost too. The presentation of the method is accompanied by an analysis that helps to discover some intriguing properties of the MovieLens data set, that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender system (e.g. [1, 3]). |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://webmining.spd.louisville.edu/webkdd06/slides/Augusto--Pucci-WEBKDD2006.pdf |
| Alternate Webpage(s) | http://webmining.spd.louisville.edu/webkdd06/papers/paper-10-a-Random-Walk-based-scoring-alg-with-app-recomm-large-scale-e-commerce-WM_1072%5B1%5D.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
